OptaPlanner User Guide

Conflict: A team can only play once per day.
Unavailability: Some teams are unavailable on some dates.

Fair assignment: All teams should play an (almost) equal number of times.

Schedule each lecture into a timeslot and into a room.

Evenly confrontation: Each team should play against every other team an equal number of times.

3.2.5.2. Problem size

munich-7teams has 7 teams, 18 days, 12 unavailabilityPenalties and 72 teamAssignments with a search space of 10^60.

3.3. Real examples

3.3.1. Course timetabling (ITC 2007 track 3 - Curriculum course scheduling)

3.3.1.1. Problem statement

Teacher conflict: A teacher must not have 2 lectures in the same period.
Curriculum conflict: A curriculum must not have 2 lectures in the same period.
Room occupancy: 2 lectures must not be in the same room in the same period.
Unavailable period (specified per dataset): A specific lecture must not be assigned to a specific period.

The problem is defined by the International Timetabling Competition 2007 track 3.

Room capacity: A room's capacity should not be less than the number of students in its lecture.
Minimum working days: Lectures of the same course should be spread into a minimum number of days.
Curriculum compactness: Lectures belonging to the same curriculum should be adjacent to each other (so in consecutive periods).
Room stability: Lectures of the same course should be assigned the same room.

3.3.1.2. Problem size

comp01 has 24 teachers,  14 curricula,  30 courses, 160 lectures, 30 periods,  6 rooms and   53 unavailable period constraints with a search space of  10^360.
comp02 has 71 teachers,  70 curricula,  82 courses, 283 lectures, 25 periods, 16 rooms and  513 unavailable period constraints with a search space of  10^736.
comp03 has 61 teachers,  68 curricula,  72 courses, 251 lectures, 25 periods, 16 rooms and  382 unavailable period constraints with a search space of  10^653.
comp04 has 70 teachers,  57 curricula,  79 courses, 286 lectures, 25 periods, 18 rooms and  396 unavailable period constraints with a search space of  10^758.
comp05 has 47 teachers, 139 curricula,  54 courses, 152 lectures, 36 periods,  9 rooms and  771 unavailable period constraints with a search space of  10^381.
comp06 has 87 teachers,  70 curricula, 108 courses, 361 lectures, 25 periods, 18 rooms and  632 unavailable period constraints with a search space of  10^957.
comp07 has 99 teachers,  77 curricula, 131 courses, 434 lectures, 25 periods, 20 rooms and  667 unavailable period constraints with a search space of 10^1171.
comp08 has 76 teachers,  61 curricula,  86 courses, 324 lectures, 25 periods, 18 rooms and  478 unavailable period constraints with a search space of  10^859.
comp09 has 68 teachers,  75 curricula,  76 courses, 279 lectures, 25 periods, 18 rooms and  405 unavailable period constraints with a search space of  10^740.
comp10 has 88 teachers,  67 curricula, 115 courses, 370 lectures, 25 periods, 18 rooms and  694 unavailable period constraints with a search space of  10^981.
comp11 has 24 teachers,  13 curricula,  30 courses, 162 lectures, 45 periods,  5 rooms and   94 unavailable period constraints with a search space of  10^381.
comp12 has 74 teachers, 150 curricula,  88 courses, 218 lectures, 36 periods, 11 rooms and 1368 unavailable period constraints with a search space of  10^566.
comp13 has 77 teachers,  66 curricula,  82 courses, 308 lectures, 25 periods, 19 rooms and  468 unavailable period constraints with a search space of  10^824.
comp14 has 68 teachers,  60 curricula,  85 courses, 275 lectures, 25 periods, 17 rooms and  486 unavailable period constraints with a search space of  10^722.

3.3.1.3. Domain model

3.3.2. Machine reassignment (Google ROADEF 2012)

3.3.2.1. Problem statement

Assign each process to a machine. All processes already have an original (unoptimized) assignment. Each process requires an amount of each resource (such as CPU, RAM, ...). This is more complex version of the Cloud Balancing example.

Maximum capacity: The maximum capacity for each resource for each machine must not be exceeded.
Conflict: Processes of the same service must run on distinct machines.
Spread: Processes of the same service must be spread across locations.
Dependency: The processes of a service depending on another service must run in the neighborhood of a process of the other service.
Transient usage: Some resources are transient and count towards the maximum capacity of both the original machine as the newly assigned machine.

The problem is defined by the Google ROADEF/EURO Challenge 2012.

Load: The safety capacity for each resource for each machine should not be exceeded.
Balance: Leave room for future assignments by balancing the available resources on each machine.
Process move cost: A process has a move cost.
Service move cost: A service has a move cost.
Machine move cost: Moving a process from machine A to machine B has another A-B specific move cost.

3.3.2.2. Problem size

model_a1_1 has  2 resources,  1 neighborhoods,   4 locations,    4 machines,    79 services,   100 processes and 1 balancePenalties with a search space of     10^60.
model_a1_2 has  4 resources,  2 neighborhoods,   4 locations,  100 machines,   980 services,  1000 processes and 0 balancePenalties with a search space of   10^2000.
model_a1_3 has  3 resources,  5 neighborhoods,  25 locations,  100 machines,   216 services,  1000 processes and 0 balancePenalties with a search space of   10^2000.
model_a1_4 has  3 resources, 50 neighborhoods,  50 locations,   50 machines,   142 services,  1000 processes and 1 balancePenalties with a search space of   10^1698.
model_a1_5 has  4 resources,  2 neighborhoods,   4 locations,   12 machines,   981 services,  1000 processes and 1 balancePenalties with a search space of   10^1079.
model_a2_1 has  3 resources,  1 neighborhoods,   1 locations,  100 machines,  1000 services,  1000 processes and 0 balancePenalties with a search space of   10^2000.
model_a2_2 has 12 resources,  5 neighborhoods,  25 locations,  100 machines,   170 services,  1000 processes and 0 balancePenalties with a search space of   10^2000.
model_a2_3 has 12 resources,  5 neighborhoods,  25 locations,  100 machines,   129 services,  1000 processes and 0 balancePenalties with a search space of   10^2000.
model_a2_4 has 12 resources,  5 neighborhoods,  25 locations,   50 machines,   180 services,  1000 processes and 1 balancePenalties with a search space of   10^1698.
model_a2_5 has 12 resources,  5 neighborhoods,  25 locations,   50 machines,   153 services,  1000 processes and 0 balancePenalties with a search space of   10^1698.
model_b_1  has 12 resources,  5 neighborhoods,  10 locations,  100 machines,  2512 services,  5000 processes and 0 balancePenalties with a search space of  10^10000.
model_b_2  has 12 resources,  5 neighborhoods,  10 locations,  100 machines,  2462 services,  5000 processes and 1 balancePenalties with a search space of  10^10000.
model_b_3  has  6 resources,  5 neighborhoods,  10 locations,  100 machines, 15025 services, 20000 processes and 0 balancePenalties with a search space of  10^40000.
model_b_4  has  6 resources,  5 neighborhoods,  50 locations,  500 machines,  1732 services, 20000 processes and 1 balancePenalties with a search space of  10^53979.
model_b_5  has  6 resources,  5 neighborhoods,  10 locations,  100 machines, 35082 services, 40000 processes and 0 balancePenalties with a search space of  10^80000.
model_b_6  has  6 resources,  5 neighborhoods,  50 locations,  200 machines, 14680 services, 40000 processes and 1 balancePenalties with a search space of  10^92041.
model_b_7  has  6 resources,  5 neighborhoods,  50 locations, 4000 machines, 15050 services, 40000 processes and 1 balancePenalties with a search space of 10^144082.
model_b_8  has  3 resources,  5 neighborhoods,  10 locations,  100 machines, 45030 services, 50000 processes and 0 balancePenalties with a search space of 10^100000.
model_b_9  has  3 resources,  5 neighborhoods, 100 locations, 1000 machines,  4609 services, 50000 processes and 1 balancePenalties with a search space of 10^150000.
model_b_10 has  3 resources,  5 neighborhoods, 100 locations, 5000 machines,  4896 services, 50000 processes and 1 balancePenalties with a search space of 10^184948.

3.3.2.3. Domain model

3.3.3. Vehicle routing

3.3.3.1. Problem statement

Using a fleet of vehicles, pick up the objects of each customer and bring them to the depot. Each vehicle can service multiple customers, but it has a limited capacity.

Besides the basic case (CVRP), there is also a variant with time windows (CVRPTW).

Vehicle capacity: a vehicle cannot carry more items then its capacity.
Time windows (only in CVRPTW):

The capacitated vehicle routing problem (CVRP) and it's timewindowed variant (CVRPTW) are defined by the VRP web.

Total distance: minimize the total distance driven (fuel consumption) of all vehicles.

3.3.3.2. Problem size

CVRP instances (without time windows):

A-n32-k5  has 1 depots,  5 vehicles and  31 customers with a search space of  10^46.
A-n33-k5  has 1 depots,  5 vehicles and  32 customers with a search space of  10^48.
A-n33-k6  has 1 depots,  6 vehicles and  32 customers with a search space of  10^48.
A-n34-k5  has 1 depots,  5 vehicles and  33 customers with a search space of  10^50.
A-n36-k5  has 1 depots,  5 vehicles and  35 customers with a search space of  10^54.
A-n37-k5  has 1 depots,  5 vehicles and  36 customers with a search space of  10^56.
A-n37-k6  has 1 depots,  6 vehicles and  36 customers with a search space of  10^56.
A-n38-k5  has 1 depots,  5 vehicles and  37 customers with a search space of  10^58.
A-n39-k5  has 1 depots,  5 vehicles and  38 customers with a search space of  10^60.
A-n39-k6  has 1 depots,  6 vehicles and  38 customers with a search space of  10^60.
A-n44-k7  has 1 depots,  7 vehicles and  43 customers with a search space of  10^70.
A-n45-k6  has 1 depots,  6 vehicles and  44 customers with a search space of  10^72.
A-n45-k7  has 1 depots,  7 vehicles and  44 customers with a search space of  10^72.
A-n46-k7  has 1 depots,  7 vehicles and  45 customers with a search space of  10^74.
A-n48-k7  has 1 depots,  7 vehicles and  47 customers with a search space of  10^78.
A-n53-k7  has 1 depots,  7 vehicles and  52 customers with a search space of  10^89.
A-n54-k7  has 1 depots,  7 vehicles and  53 customers with a search space of  10^91.
A-n55-k9  has 1 depots,  9 vehicles and  54 customers with a search space of  10^93.
A-n60-k9  has 1 depots,  9 vehicles and  59 customers with a search space of 10^104.
A-n61-k9  has 1 depots,  9 vehicles and  60 customers with a search space of 10^106.
A-n62-k8  has 1 depots,  8 vehicles and  61 customers with a search space of 10^108.
A-n63-k10 has 1 depots, 10 vehicles and  62 customers with a search space of 10^111.
A-n63-k9  has 1 depots,  9 vehicles and  62 customers with a search space of 10^111.
A-n64-k9  has 1 depots,  9 vehicles and  63 customers with a search space of 10^113.
A-n65-k9  has 1 depots,  9 vehicles and  64 customers with a search space of 10^115.
A-n69-k9  has 1 depots,  9 vehicles and  68 customers with a search space of 10^124.
A-n80-k10 has 1 depots, 10 vehicles and  79 customers with a search space of 10^149.
F-n135-k7 has 1 depots,  7 vehicles and 134 customers with a search space of 10^285.
F-n45-k4  has 1 depots,  4 vehicles and  44 customers with a search space of  10^72.
F-n72-k4  has 1 depots,  4 vehicles and  71 customers with a search space of 10^131.

CVRPTW instances (with time windows):

Solomon_025_C101       has 1 depots,  25 vehicles and   25 customers with a search space of   10^34.
Solomon_025_C201       has 1 depots,  25 vehicles and   25 customers with a search space of   10^34.
Solomon_025_R101       has 1 depots,  25 vehicles and   25 customers with a search space of   10^34.
Solomon_025_R201       has 1 depots,  25 vehicles and   25 customers with a search space of   10^34.
Solomon_025_RC101      has 1 depots,  25 vehicles and   25 customers with a search space of   10^34.
Solomon_025_RC201      has 1 depots,  25 vehicles and   25 customers with a search space of   10^34.
Solomon_100_C101       has 1 depots,  25 vehicles and  100 customers with a search space of  10^200.
Solomon_100_C201       has 1 depots,  25 vehicles and  100 customers with a search space of  10^200.
Solomon_100_R101       has 1 depots,  25 vehicles and  100 customers with a search space of  10^200.
Solomon_100_R201       has 1 depots,  25 vehicles and  100 customers with a search space of  10^200.
Solomon_100_RC101      has 1 depots,  25 vehicles and  100 customers with a search space of  10^200.
Solomon_100_RC201      has 1 depots,  25 vehicles and  100 customers with a search space of  10^200.
Homberger_0200_C1_2_1  has 1 depots,  50 vehicles and  200 customers with a search space of  10^460.
Homberger_0200_C2_2_1  has 1 depots,  50 vehicles and  200 customers with a search space of  10^460.
Homberger_0200_R1_2_1  has 1 depots,  50 vehicles and  200 customers with a search space of  10^460.
Homberger_0200_R2_2_1  has 1 depots,  50 vehicles and  200 customers with a search space of  10^460.
Homberger_0200_RC1_2_1 has 1 depots,  50 vehicles and  200 customers with a search space of  10^460.
Homberger_0200_RC2_2_1 has 1 depots,  50 vehicles and  200 customers with a search space of  10^460.
Homberger_0400_C1_4_1  has 1 depots, 100 vehicles and  400 customers with a search space of 10^1040.
Homberger_0400_C2_4_1  has 1 depots, 100 vehicles and  400 customers with a search space of 10^1040.
Homberger_0400_R1_4_1  has 1 depots, 100 vehicles and  400 customers with a search space of 10^1040.
Homberger_0400_R2_4_1  has 1 depots, 100 vehicles and  400 customers with a search space of 10^1040.
Homberger_0400_RC1_4_1 has 1 depots, 100 vehicles and  400 customers with a search space of 10^1040.
Homberger_0400_RC2_4_1 has 1 depots, 100 vehicles and  400 customers with a search space of 10^1040.
Homberger_0600_C1_6_1  has 1 depots, 150 vehicles and  600 customers with a search space of 10^1666.
Homberger_0600_C2_6_1  has 1 depots, 150 vehicles and  600 customers with a search space of 10^1666.
Homberger_0600_R1_6_1  has 1 depots, 150 vehicles and  600 customers with a search space of 10^1666.
Homberger_0600_R2_6_1  has 1 depots, 150 vehicles and  600 customers with a search space of 10^1666.
Homberger_0600_RC1_6_1 has 1 depots, 150 vehicles and  600 customers with a search space of 10^1666.
Homberger_0600_RC2_6_1 has 1 depots, 150 vehicles and  600 customers with a search space of 10^1666.
Homberger_0800_C1_8_1  has 1 depots, 200 vehicles and  800 customers with a search space of 10^2322.
Homberger_0800_C2_8_1  has 1 depots, 200 vehicles and  800 customers with a search space of 10^2322.
Homberger_0800_R1_8_1  has 1 depots, 200 vehicles and  800 customers with a search space of 10^2322.
Homberger_0800_R2_8_1  has 1 depots, 200 vehicles and  800 customers with a search space of 10^2322.
Homberger_0800_RC1_8_1 has 1 depots, 200 vehicles and  800 customers with a search space of 10^2322.
Homberger_0800_RC2_8_1 has 1 depots, 200 vehicles and  800 customers with a search space of 10^2322.
Homberger_1000_C110_1  has 1 depots, 250 vehicles and 1000 customers with a search space of 10^3000.
Homberger_1000_C210_1  has 1 depots, 250 vehicles and 1000 customers with a search space of 10^3000.
Homberger_1000_R110_1  has 1 depots, 250 vehicles and 1000 customers with a search space of 10^3000.
Homberger_1000_R210_1  has 1 depots, 250 vehicles and 1000 customers with a search space of 10^3000.
Homberger_1000_RC110_1 has 1 depots, 250 vehicles and 1000 customers with a search space of 10^3000.
Homberger_1000_RC210_1 has 1 depots, 250 vehicles and 1000 customers with a search space of 10^3000.

3.3.3.3. Domain model

The vehicle routing with timewindows domain model makes heavily use of shadow variables. This allows it to express its constraints more naturally, because properties such as arrivalTime and departureTime, are directly available on the domain model.

3.3.3.4. Road distances instead of air distances

In the real world, vehicles can't follow a straight line from location to location: they have to use roads and highways. From a business point of view, this matters a lot:

For the optimization algorithm, this doesn't matter much, as long as the distance between 2 points can be looked up (and are preferably precalculated). The road cost doesn't even need to be a distance, it can also be travel time, fuel cost, or a weighted function of those. There are several technologies available to precalculate road costs, such as GraphHopper (embeddable, offline Java engine) and Open MapQuest (webservice). There are also several technologies to render it, such as Leaflet.

3.3.4. Project job scheduling

3.3.4.1. Problem statement

Schedule all jobs in time and execution mode to minimize project delays. Each job is part of a project. A job can be executed in different ways: each way is an execution mode that implies a different duration but also different resource usages. This is a form of flexible job shop scheduling.

Job precedence: a job can only start when all its predecessor jobs are finished.
Resource capacity: do not use more resources then available.
- Resources are local (shared between jobs of the same project) or global (shared between all jobs)
- Resource are renewable (capacity available per day) or nonrenewable (capacity available for all days)

Total project delay: minimize the duration (makespan) of each project.

The problem is defined by the MISTA 2013 challenge.

Total makespan: minimize the duration of the whole multi-project schedule.

3.3.4.2. Problem size

Schedule A-1  has  2 projects,  24 jobs,   64 execution modes,  7 resources and  150 resource requirements.
Schedule A-2  has  2 projects,  44 jobs,  124 execution modes,  7 resources and  420 resource requirements.
Schedule A-3  has  2 projects,  64 jobs,  184 execution modes,  7 resources and  630 resource requirements.
Schedule A-4  has  5 projects,  60 jobs,  160 execution modes, 16 resources and  390 resource requirements.
Schedule A-5  has  5 projects, 110 jobs,  310 execution modes, 16 resources and  900 resource requirements.
Schedule A-6  has  5 projects, 160 jobs,  460 execution modes, 16 resources and 1440 resource requirements.
Schedule A-7  has 10 projects, 120 jobs,  320 execution modes, 22 resources and  900 resource requirements.
Schedule A-8  has 10 projects, 220 jobs,  620 execution modes, 22 resources and 1860 resource requirements.
Schedule A-9  has 10 projects, 320 jobs,  920 execution modes, 31 resources and 2880 resource requirements.
Schedule A-10 has 10 projects, 320 jobs,  920 execution modes, 31 resources and 2970 resource requirements.
Schedule B-1  has 10 projects, 120 jobs,  320 execution modes, 31 resources and  900 resource requirements.
Schedule B-2  has 10 projects, 220 jobs,  620 execution modes, 22 resources and 1740 resource requirements.
Schedule B-3  has 10 projects, 320 jobs,  920 execution modes, 31 resources and 3060 resource requirements.
Schedule B-4  has 15 projects, 180 jobs,  480 execution modes, 46 resources and 1530 resource requirements.
Schedule B-5  has 15 projects, 330 jobs,  930 execution modes, 46 resources and 2760 resource requirements.
Schedule B-6  has 15 projects, 480 jobs, 1380 execution modes, 46 resources and 4500 resource requirements.
Schedule B-7  has 20 projects, 240 jobs,  640 execution modes, 61 resources and 1710 resource requirements.
Schedule B-8  has 20 projects, 440 jobs, 1240 execution modes, 42 resources and 3180 resource requirements.
Schedule B-9  has 20 projects, 640 jobs, 1840 execution modes, 61 resources and 5940 resource requirements.
Schedule B-10 has 20 projects, 460 jobs, 1300 execution modes, 42 resources and 4260 resource requirements.

3.3.5. Hospital bed planning (PAS - Patient admission scheduling)

3.3.5.1. Problem statement

Assign each patient (that will come to the hospital) into a bed for each night that the patient will stay in the hospital. Each bed belongs to a room and each room belongs to a department. The arrival and departure dates of the patients is fixed: only a bed needs to be assigned for each night.

This problem features overconstrained datasets.

2 patients must not be assigned to the same bed in the same night. Weight: -1000hard * conflictNightCount.
A room can have a gender limitation: only females, only males, the same gender in the same night or no gender limitation at all. Weight: -50hard * nightCount.
A department can have a minimum or maximum age. Weight: -100hard * nightCount.
A patient can require a room with specific equipment(s). Weight: -50hard * nightCount.

Assign every patient to a bed, unless the dataset is overconstrained. Weight: -1medium * nightCount.

Schedule each exam into a period and into a room. Multiple exams can share the same room during the same period.

A patient can prefer a maximum room size, for example if he/she want a single room. Weight: -8soft * nightCount.
A patient is best assigned to a department that specializes in his/her problem. Weight: -10soft * nightCount.
A patient is best assigned to a room that specializes in his/her problem. Weight: -20soft * nightCount.
- That room specialism should be priority 1. Weight: -10soft * (priority - 1) * nightCount.
A patient can prefer a room with specific equipment(s). Weight: -20soft * nightCount.

The problem is a variant on Kaho's Patient Scheduling and the datasets come from real world hospitals.

3.3.5.2. Problem size

testdata01 has 4 specialisms, 2 equipments, 4 departments,  98 rooms, 286 beds, 14 nights,  652 patients and  652 admissions with a search space of 10^1601.
testdata02 has 6 specialisms, 2 equipments, 6 departments, 151 rooms, 465 beds, 14 nights,  755 patients and  755 admissions with a search space of 10^2013.
testdata03 has 5 specialisms, 2 equipments, 5 departments, 131 rooms, 395 beds, 14 nights,  708 patients and  708 admissions with a search space of 10^1838.
testdata04 has 6 specialisms, 2 equipments, 6 departments, 155 rooms, 471 beds, 14 nights,  746 patients and  746 admissions with a search space of 10^1994.
testdata05 has 4 specialisms, 2 equipments, 4 departments, 102 rooms, 325 beds, 14 nights,  587 patients and  587 admissions with a search space of 10^1474.
testdata06 has 4 specialisms, 2 equipments, 4 departments, 104 rooms, 313 beds, 14 nights,  685 patients and  685 admissions with a search space of 10^1709.
testdata07 has 6 specialisms, 4 equipments, 6 departments, 162 rooms, 472 beds, 14 nights,  519 patients and  519 admissions with a search space of 10^1387.
testdata08 has 6 specialisms, 4 equipments, 6 departments, 148 rooms, 441 beds, 21 nights,  895 patients and  895 admissions with a search space of 10^2366.
testdata09 has 4 specialisms, 4 equipments, 4 departments, 105 rooms, 310 beds, 28 nights, 1400 patients and 1400 admissions with a search space of 10^3487.
testdata10 has 4 specialisms, 4 equipments, 4 departments, 104 rooms, 308 beds, 56 nights, 1575 patients and 1575 admissions with a search space of 10^3919.
testdata11 has 4 specialisms, 4 equipments, 4 departments, 107 rooms, 318 beds, 91 nights, 2514 patients and 2514 admissions with a search space of 10^6291.
testdata12 has 4 specialisms, 4 equipments, 4 departments, 105 rooms, 310 beds, 84 nights, 2750 patients and 2750 admissions with a search space of 10^6851.
testdata13 has 5 specialisms, 4 equipments, 5 departments, 125 rooms, 368 beds, 28 nights,  907 patients and 1109 admissions with a search space of 10^2845.

3.4. Difficult examples

3.4.1. Exam timetabling (ITC 2007 track 1 - Examination)

3.4.1.1. Problem statement

Soft constraints (each of which has a parametrized penalty):

Exam conflict: 2 exams that share students must not occur in the same period.
Room capacity: A room's seating capacity must suffice at all times.
Period duration: A period's duration must suffice for all of its exams.
Period related hard constraints (specified per dataset):
Room related hard constraints (specified per dataset):
- Exclusive: 1 specified exam should not have to share its room with any other exam.

The same student should not have 2 exams in a row.
The same student should not have 2 exams on the same day.
Period spread: 2 exams that share students should be a number of periods apart.
Mixed durations: 2 exams that share a room should not have different durations.
Front load: Large exams should be scheduled earlier in the schedule.
Period penalty (specified per dataset): Some periods have a penalty when used.
Room penalty (specified per dataset): Some rooms have a penalty when used.

It uses large test data sets of real-life universities.

The problem is defined by the International Timetabling Competition 2007 track 1. Geoffrey De Smet finished 4th in that competition with a very early version of OptaPlanner. Many improvements have been made since then.

3.4.1.2. Problem size

exam_comp_set1 has  7883 students,  607 exams, 54 periods,  7 rooms,  12 period constraints and  0 room constraints with a search space of 10^1564.
exam_comp_set2 has 12484 students,  870 exams, 40 periods, 49 rooms,  12 period constraints and  2 room constraints with a search space of 10^2864.
exam_comp_set3 has 16365 students,  934 exams, 36 periods, 48 rooms, 168 period constraints and 15 room constraints with a search space of 10^3023.
exam_comp_set4 has  4421 students,  273 exams, 21 periods,  1 rooms,  40 period constraints and  0 room constraints with a search space of  10^360.
exam_comp_set5 has  8719 students, 1018 exams, 42 periods,  3 rooms,  27 period constraints and  0 room constraints with a search space of 10^2138.
exam_comp_set6 has  7909 students,  242 exams, 16 periods,  8 rooms,  22 period constraints and  0 room constraints with a search space of  10^509.
exam_comp_set7 has 13795 students, 1096 exams, 80 periods, 15 rooms,  28 period constraints and  0 room constraints with a search space of 10^3374.
exam_comp_set8 has  7718 students,  598 exams, 80 periods,  8 rooms,  20 period constraints and  1 room constraints with a search space of 10^1678.

3.4.1.3. Domain model

Below you can see the main examination domain classes:

Figure 3.3. Examination domain class diagram

Notice that we've split up the exam concept into an Exam class and a Topic class. The Exam instances change during solving (this is the planning entity class), when their period or room property changes. The Topic, Period and Room instances never change during solving (these are problem facts, just like some other classes).

3.4.2. Employee rostering (INRC 2010 - Nurse rostering)

3.4.2.1. Problem statement

For each shift, assign a nurse to work that shift.

The problem is defined by the International Nurse Rostering Competition 2010.

3.4.2.2. Problem size

There are 3 dataset types:

sprint: must be solved in seconds.
medium: must be solved in minutes.
long: must be solved in hours.

toy1          has 1 skills, 3 shiftTypes, 2 patterns, 1 contracts,  6 employees,  7 shiftDates,  35 shiftAssignments and   0 requests with a search space of   10^27.
toy2          has 1 skills, 3 shiftTypes, 3 patterns, 2 contracts, 20 employees, 28 shiftDates, 180 shiftAssignments and 140 requests with a search space of  10^234.

sprint01      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint02      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint03      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint04      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint05      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint06      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint07      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint08      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint09      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint10      has 1 skills, 4 shiftTypes, 3 patterns, 4 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_hint01 has 1 skills, 4 shiftTypes, 8 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_hint02 has 1 skills, 4 shiftTypes, 0 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_hint03 has 1 skills, 4 shiftTypes, 8 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_late01 has 1 skills, 4 shiftTypes, 8 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_late02 has 1 skills, 3 shiftTypes, 4 patterns, 3 contracts, 10 employees, 28 shiftDates, 144 shiftAssignments and 139 requests with a search space of  10^144.
sprint_late03 has 1 skills, 4 shiftTypes, 8 patterns, 3 contracts, 10 employees, 28 shiftDates, 160 shiftAssignments and 150 requests with a search space of  10^160.
sprint_late04 has 1 skills, 4 shiftTypes, 8 patterns, 3 contracts, 10 employees, 28 shiftDates, 160 shiftAssignments and 150 requests with a search space of  10^160.
sprint_late05 has 1 skills, 4 shiftTypes, 8 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_late06 has 1 skills, 4 shiftTypes, 0 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_late07 has 1 skills, 4 shiftTypes, 0 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.
sprint_late08 has 1 skills, 4 shiftTypes, 0 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and   0 requests with a search space of  10^152.
sprint_late09 has 1 skills, 4 shiftTypes, 0 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and   0 requests with a search space of  10^152.
sprint_late10 has 1 skills, 4 shiftTypes, 0 patterns, 3 contracts, 10 employees, 28 shiftDates, 152 shiftAssignments and 150 requests with a search space of  10^152.

medium01      has 1 skills, 4 shiftTypes, 0 patterns, 4 contracts, 31 employees, 28 shiftDates, 608 shiftAssignments and 403 requests with a search space of  10^906.
medium02      has 1 skills, 4 shiftTypes, 0 patterns, 4 contracts, 31 employees, 28 shiftDates, 608 shiftAssignments and 403 requests with a search space of  10^906.
medium03      has 1 skills, 4 shiftTypes, 0 patterns, 4 contracts, 31 employees, 28 shiftDates, 608 shiftAssignments and 403 requests with a search space of  10^906.
medium04      has 1 skills, 4 shiftTypes, 0 patterns, 4 contracts, 31 employees, 28 shiftDates, 608 shiftAssignments and 403 requests with a search space of  10^906.
medium05      has 1 skills, 4 shiftTypes, 0 patterns, 4 contracts, 31 employees, 28 shiftDates, 608 shiftAssignments and 403 requests with a search space of  10^906.
medium_hint01 has 1 skills, 4 shiftTypes, 7 patterns, 4 contracts, 30 employees, 28 shiftDates, 428 shiftAssignments and 390 requests with a search space of  10^632.
medium_hint02 has 1 skills, 4 shiftTypes, 7 patterns, 3 contracts, 30 employees, 28 shiftDates, 428 shiftAssignments and 390 requests with a search space of  10^632.
medium_hint03 has 1 skills, 4 shiftTypes, 7 patterns, 4 contracts, 30 employees, 28 shiftDates, 428 shiftAssignments and 390 requests with a search space of  10^632.
medium_late01 has 1 skills, 4 shiftTypes, 7 patterns, 4 contracts, 30 employees, 28 shiftDates, 424 shiftAssignments and 390 requests with a search space of  10^626.
medium_late02 has 1 skills, 4 shiftTypes, 7 patterns, 3 contracts, 30 employees, 28 shiftDates, 428 shiftAssignments and 390 requests with a search space of  10^632.
medium_late03 has 1 skills, 4 shiftTypes, 0 patterns, 4 contracts, 30 employees, 28 shiftDates, 428 shiftAssignments and 390 requests with a search space of  10^632.
medium_late04 has 1 skills, 4 shiftTypes, 7 patterns, 3 contracts, 30 employees, 28 shiftDates, 416 shiftAssignments and 390 requests with a search space of  10^614.
medium_late05 has 2 skills, 5 shiftTypes, 7 patterns, 4 contracts, 30 employees, 28 shiftDates, 452 shiftAssignments and 390 requests with a search space of  10^667.

long01        has 2 skills, 5 shiftTypes, 3 patterns, 3 contracts, 49 employees, 28 shiftDates, 740 shiftAssignments and 735 requests with a search space of 10^1250.
long02        has 2 skills, 5 shiftTypes, 3 patterns, 3 contracts, 49 employees, 28 shiftDates, 740 shiftAssignments and 735 requests with a search space of 10^1250.
long03        has 2 skills, 5 shiftTypes, 3 patterns, 3 contracts, 49 employees, 28 shiftDates, 740 shiftAssignments and 735 requests with a search space of 10^1250.
long04        has 2 skills, 5 shiftTypes, 3 patterns, 3 contracts, 49 employees, 28 shiftDates, 740 shiftAssignments and 735 requests with a search space of 10^1250.
long05        has 2 skills, 5 shiftTypes, 3 patterns, 3 contracts, 49 employees, 28 shiftDates, 740 shiftAssignments and 735 requests with a search space of 10^1250.
long_hint01   has 2 skills, 5 shiftTypes, 9 patterns, 3 contracts, 50 employees, 28 shiftDates, 740 shiftAssignments and   0 requests with a search space of 10^1257.
long_hint02   has 2 skills, 5 shiftTypes, 7 patterns, 3 contracts, 50 employees, 28 shiftDates, 740 shiftAssignments and   0 requests with a search space of 10^1257.
long_hint03   has 2 skills, 5 shiftTypes, 7 patterns, 3 contracts, 50 employees, 28 shiftDates, 740 shiftAssignments and   0 requests with a search space of 10^1257.
long_late01   has 2 skills, 5 shiftTypes, 9 patterns, 3 contracts, 50 employees, 28 shiftDates, 752 shiftAssignments and   0 requests with a search space of 10^1277.
long_late02   has 2 skills, 5 shiftTypes, 9 patterns, 4 contracts, 50 employees, 28 shiftDates, 752 shiftAssignments and   0 requests with a search space of 10^1277.
long_late03   has 2 skills, 5 shiftTypes, 9 patterns, 3 contracts, 50 employees, 28 shiftDates, 752 shiftAssignments and   0 requests with a search space of 10^1277.
long_late04   has 2 skills, 5 shiftTypes, 9 patterns, 4 contracts, 50 employees, 28 shiftDates, 752 shiftAssignments and   0 requests with a search space of 10^1277.
long_late05   has 2 skills, 5 shiftTypes, 9 patterns, 3 contracts, 50 employees, 28 shiftDates, 740 shiftAssignments and   0 requests with a search space of 10^1257.

3.4.3. Traveling tournament problem (TTP)

3.4.3.1. Problem statement

Schedule matches between n teams.

Each team plays twice against every other team: once home and once away.
Each team has exactly 1 match on each timeslot.
No team must have more than 3 consecutive home or 3 consecutive away matches.
No repeaters: no 2 consecutive matches of the same 2 opposing teams.

The problem is defined on Michael Trick's website (which contains the world records too).

Minimize the total distance traveled by all teams.

3.4.3.2. Problem size

1-nl04     has  6 days,  4 teams and   12 matches with a search space of    10^9.
1-nl06     has 10 days,  6 teams and   30 matches with a search space of   10^30.
1-nl08     has 14 days,  8 teams and   56 matches with a search space of   10^64.
1-nl10     has 18 days, 10 teams and   90 matches with a search space of  10^112.
1-nl12     has 22 days, 12 teams and  132 matches with a search space of  10^177.
1-nl14     has 26 days, 14 teams and  182 matches with a search space of  10^257.
1-nl16     has 30 days, 16 teams and  240 matches with a search space of  10^354.
2-bra24    has 46 days, 24 teams and  552 matches with a search space of  10^917.
3-nfl16    has 30 days, 16 teams and  240 matches with a search space of  10^354.
3-nfl18    has 34 days, 18 teams and  306 matches with a search space of  10^468.
3-nfl20    has 38 days, 20 teams and  380 matches with a search space of  10^600.
3-nfl22    has 42 days, 22 teams and  462 matches with a search space of  10^749.
3-nfl24    has 46 days, 24 teams and  552 matches with a search space of  10^917.
3-nfl26    has 50 days, 26 teams and  650 matches with a search space of 10^1104.
3-nfl28    has 54 days, 28 teams and  756 matches with a search space of 10^1309.
3-nfl30    has 58 days, 30 teams and  870 matches with a search space of 10^1534.
3-nfl32    has 62 days, 32 teams and  992 matches with a search space of 10^1778.
4-super04  has  6 days,  4 teams and   12 matches with a search space of    10^9.
4-super06  has 10 days,  6 teams and   30 matches with a search space of   10^30.
4-super08  has 14 days,  8 teams and   56 matches with a search space of   10^64.
4-super10  has 18 days, 10 teams and   90 matches with a search space of  10^112.
4-super12  has 22 days, 12 teams and  132 matches with a search space of  10^177.
4-super14  has 26 days, 14 teams and  182 matches with a search space of  10^257.
5-galaxy04 has  6 days,  4 teams and   12 matches with a search space of    10^9.
5-galaxy06 has 10 days,  6 teams and   30 matches with a search space of   10^30.
5-galaxy08 has 14 days,  8 teams and   56 matches with a search space of   10^64.
5-galaxy10 has 18 days, 10 teams and   90 matches with a search space of  10^112.
5-galaxy12 has 22 days, 12 teams and  132 matches with a search space of  10^177.
5-galaxy14 has 26 days, 14 teams and  182 matches with a search space of  10^257.
5-galaxy16 has 30 days, 16 teams and  240 matches with a search space of  10^354.
5-galaxy18 has 34 days, 18 teams and  306 matches with a search space of  10^468.
5-galaxy20 has 38 days, 20 teams and  380 matches with a search space of  10^600.
5-galaxy22 has 42 days, 22 teams and  462 matches with a search space of  10^749.
5-galaxy24 has 46 days, 24 teams and  552 matches with a search space of  10^917.
5-galaxy26 has 50 days, 26 teams and  650 matches with a search space of 10^1104.
5-galaxy28 has 54 days, 28 teams and  756 matches with a search space of 10^1309.
5-galaxy30 has 58 days, 30 teams and  870 matches with a search space of 10^1534.
5-galaxy32 has 62 days, 32 teams and  992 matches with a search space of 10^1778.
5-galaxy34 has 66 days, 34 teams and 1122 matches with a search space of 10^2041.
5-galaxy36 has 70 days, 36 teams and 1260 matches with a search space of 10^2324.
5-galaxy38 has 74 days, 38 teams and 1406 matches with a search space of 10^2628.
5-galaxy40 has 78 days, 40 teams and 1560 matches with a search space of 10^2951.

3.4.4. Cheap time scheduling

3.4.4.1. Problem statement

Schedule all tasks in time and on a machine to minimize power cost. Power prices differs in time. This is a form of job shop scheduling.

Start time limits: each task must start between its earliest start and latest start limit.
Maximum capacity: the maximum capacity for each resource for each machine must not be exceeded.
Startup and shutdown: each machine must be active in the periods during which it has assigned tasks. Between tasks it is allowed to be idle to avoid startup and shutdown costs.

Soft constraints (addendum to the original problem definition):

Power cost: minimize the total power cost of the whole schedule.

Start early: prefer starting a task sooner rather than later.

The problem is defined by the ICON challenge.

3.4.4.2. Problem size

sample01   has 3 resources,   2 machines, 288 periods and   25 tasks with a search space of    10^53.
sample02   has 3 resources,   2 machines, 288 periods and   50 tasks with a search space of   10^114.
sample03   has 3 resources,   2 machines, 288 periods and  100 tasks with a search space of   10^226.
sample04   has 3 resources,   5 machines, 288 periods and  100 tasks with a search space of   10^266.
sample05   has 3 resources,   2 machines, 288 periods and  250 tasks with a search space of   10^584.
sample06   has 3 resources,   5 machines, 288 periods and  250 tasks with a search space of   10^673.
sample07   has 3 resources,   2 machines, 288 periods and 1000 tasks with a search space of  10^2388.
sample08   has 3 resources,   5 machines, 288 periods and 1000 tasks with a search space of  10^2748.
sample09   has 4 resources,  20 machines, 288 periods and 2000 tasks with a search space of  10^6668.
instance00 has 1 resources,  10 machines, 288 periods and  200 tasks with a search space of   10^595.
instance01 has 1 resources,  10 machines, 288 periods and  200 tasks with a search space of   10^599.
instance02 has 1 resources,  10 machines, 288 periods and  200 tasks with a search space of   10^599.
instance03 has 1 resources,  10 machines, 288 periods and  200 tasks with a search space of   10^591.
instance04 has 1 resources,  10 machines, 288 periods and  200 tasks with a search space of   10^590.
instance05 has 2 resources,  25 machines, 288 periods and  200 tasks with a search space of   10^667.
instance06 has 2 resources,  25 machines, 288 periods and  200 tasks with a search space of   10^660.
instance07 has 2 resources,  25 machines, 288 periods and  200 tasks with a search space of   10^662.
instance08 has 2 resources,  25 machines, 288 periods and  200 tasks with a search space of   10^651.
instance09 has 2 resources,  25 machines, 288 periods and  200 tasks with a search space of   10^659.
instance10 has 2 resources,  20 machines, 288 periods and  500 tasks with a search space of  10^1657.
instance11 has 2 resources,  20 machines, 288 periods and  500 tasks with a search space of  10^1644.
instance12 has 2 resources,  20 machines, 288 periods and  500 tasks with a search space of  10^1637.
instance13 has 2 resources,  20 machines, 288 periods and  500 tasks with a search space of  10^1659.
instance14 has 2 resources,  20 machines, 288 periods and  500 tasks with a search space of  10^1643.
instance15 has 3 resources,  40 machines, 288 periods and  500 tasks with a search space of  10^1782.
instance16 has 3 resources,  40 machines, 288 periods and  500 tasks with a search space of  10^1778.
instance17 has 3 resources,  40 machines, 288 periods and  500 tasks with a search space of  10^1764.
instance18 has 3 resources,  40 machines, 288 periods and  500 tasks with a search space of  10^1769.
instance19 has 3 resources,  40 machines, 288 periods and  500 tasks with a search space of  10^1778.
instance20 has 3 resources,  50 machines, 288 periods and 1000 tasks with a search space of  10^3689.
instance21 has 3 resources,  50 machines, 288 periods and 1000 tasks with a search space of  10^3678.
instance22 has 3 resources,  50 machines, 288 periods and 1000 tasks with a search space of  10^3706.
instance23 has 3 resources,  50 machines, 288 periods and 1000 tasks with a search space of  10^3676.
instance24 has 3 resources,  50 machines, 288 periods and 1000 tasks with a search space of  10^3681.
instance25 has 3 resources,  60 machines, 288 periods and 1000 tasks with a search space of  10^3774.
instance26 has 3 resources,  60 machines, 288 periods and 1000 tasks with a search space of  10^3737.
instance27 has 3 resources,  60 machines, 288 periods and 1000 tasks with a search space of  10^3744.
instance28 has 3 resources,  60 machines, 288 periods and 1000 tasks with a search space of  10^3731.
instance29 has 3 resources,  60 machines, 288 periods and 1000 tasks with a search space of  10^3746.
instance30 has 4 resources,  70 machines, 288 periods and 2000 tasks with a search space of  10^7718.
instance31 has 4 resources,  70 machines, 288 periods and 2000 tasks with a search space of  10^7740.
instance32 has 4 resources,  70 machines, 288 periods and 2000 tasks with a search space of  10^7686.
instance33 has 4 resources,  70 machines, 288 periods and 2000 tasks with a search space of  10^7672.
instance34 has 4 resources,  70 machines, 288 periods and 2000 tasks with a search space of  10^7695.
instance35 has 4 resources,  80 machines, 288 periods and 2000 tasks with a search space of  10^7807.
instance36 has 4 resources,  80 machines, 288 periods and 2000 tasks with a search space of  10^7814.
instance37 has 4 resources,  80 machines, 288 periods and 2000 tasks with a search space of  10^7764.
instance38 has 4 resources,  80 machines, 288 periods and 2000 tasks with a search space of  10^7736.
instance39 has 4 resources,  80 machines, 288 periods and 2000 tasks with a search space of  10^7783.
instance40 has 4 resources,  90 machines, 288 periods and 4000 tasks with a search space of 10^15976.
instance41 has 4 resources,  90 machines, 288 periods and 4000 tasks with a search space of 10^15935.
instance42 has 4 resources,  90 machines, 288 periods and 4000 tasks with a search space of 10^15887.
instance43 has 4 resources,  90 machines, 288 periods and 4000 tasks with a search space of 10^15896.
instance44 has 4 resources,  90 machines, 288 periods and 4000 tasks with a search space of 10^15885.
instance45 has 4 resources, 100 machines, 288 periods and 5000 tasks with a search space of 10^20173.
instance46 has 4 resources, 100 machines, 288 periods and 5000 tasks with a search space of 10^20132.
instance47 has 4 resources, 100 machines, 288 periods and 5000 tasks with a search space of 10^20126.
instance48 has 4 resources, 100 machines, 288 periods and 5000 tasks with a search space of 10^20110.
instance49 has 4 resources, 100 machines, 288 periods and 5000 tasks with a search space of 10^20078.

Chapter 4. Planner configuration

4.1. Overview

4.2. Solver configuration

4.2.1. Solver configuration by XML
4.2.2. Solver configuration by Java API

4.3. Model a planning problem

4.3.1. Is this class a problem fact or planning entity?
4.3.2. Problem fact
4.3.3. Planning entity
4.3.4. Planning variable
4.3.5. Planning value and planning value ranges
4.3.6. Planning problem and planning solution

4.4. Use the Solver

4.4.1. The Solver interface
4.4.2. Solving a problem
4.4.3. Environment mode: Are there bugs in my code?
4.4.4. Logging level: What is the Solver doing?
4.4.5. Random number generator

4.1. Overview

Solving a planning problem with OptaPlanner consists out of 5 steps:

Model your planning problem as a class that implements the interface Solution, for example the class NQueens.
Configure a Solver, for example a First Fit and Tabu Search solver for any NQueens instance.
Load a problem data set from your data layer, for example a 4 Queens instance. That is the planning problem.
Solve it with Solver.solve(planningProblem).
Get the best solution found by the Solver with Solver.getBestSolution().

4.2. Solver configuration

4.2.1. Solver configuration by XML

Build a Solver instance with the SolverFactory. Configure it with a solver configuration XML file, provided as a classpath resource (as definied by ClassLoader.getResource()):

       SolverFactory solverFactory = SolverFactory.createFromXmlResource(

               "org/optaplanner/examples/nqueens/solver/nqueensSolverConfig.xml");

       Solver solver = solverFactory.buildSolver();

In a typical project (following the Maven directory structure), that solverConfig XML file would be located at $PROJECT_DIR/src/main/resources/org/optaplanner/examples/nqueens/solver/nqueensSolverConfig.xml. On some environments (OSGi, JBoss modules, ...), classpath resources in your jars might not be available in by default to the classes in optaplanner's jars.

Alternatively, a SolverFactory can be created from a File, an InputStream or a Reader with methods such as SolverFactory.createFromXmlFile(). However, for portability reasons, a classpath resource is recommended.

A solver configuration file looks like this:


<?xml version="1.0" encoding="UTF-8"?>

<solver>

  <!-- Define the model -->

  <solutionClass>org.optaplanner.examples.nqueens.domain.NQueens</solutionClass>

  <entityClass>org.optaplanner.examples.nqueens.domain.Queen</entityClass>



  <!-- Define the score function -->

  <scoreDirectorFactory>

    <scoreDefinitionType>SIMPLE</scoreDefinitionType>

    <scoreDrl>org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl</scoreDrl>

  </scoreDirectorFactory>



  <!-- Configure the optimization algorithm(s) -->

  <termination>

    ...

  </termination>

  <constructionHeuristic>

    ...

  </constructionHeuristic>

  <localSearch>

    ...

  </localSearch>

</solver>

Notice the 3 parts in it:

Define the model
Define the score function
Configure the optimization algorithm(s)

These various parts of a configuration are explained further in this manual.

OptaPlanner makes it relatively easy to switch optimization algorithm(s) just by changing the configuration. There's even a Benchmarker utility which allows you to play out different configurations against each other and report the most appropriate configuration for your use case.

4.2.2. Solver configuration by Java API

A solver configuration can also be configured with the SolverConfig API. This especially useful to change some values dynamically at runtime, for example to change the running time based on user input, before building the Solver:

        SolverFactory solverFactory = SolverFactory.createFromXmlResource(

                "org/optaplanner/examples/nqueens/solver/nqueensSolverConfig.xml");


        SolverConfig solverConfig = solverFactory.getSolverConfig();

        TerminationConfig terminationConfig = solverConfig.getTerminationConfig();

        terminationConfig.setMinutesSpentLimit(userInput);


        Solver solver = solverFactory.buildSolver();

Every element in the solver configuration XML is available as a *Config class or a property on a *Config class in the package namespace org.optaplanner.core.config. These *Config classes are the Java representation of the XML format and they also provide the user-friendly way to assemble the runtime components (of the package namespace org.optaplanner.core.impl) into an efficient Solver.

4.3. Model a planning problem

4.3.1. Is this class a problem fact or planning entity?

Look at a dataset of your planning problem. You 'll recognize domain classes in there, each of which can be categorized as one of these:

A unrelated class: not used by any of the score constraints. From a planning standpoint, this data is obsolete.
A problem fact class: used by the score constraints, but does NOT change during planning (as long as the problem stays the same). For example: Bed, Room, Shift, Employee, Topic, Period, ...
A planning entity class: used by the score constraints and changes during planning. For example: BedDesignation, ShiftAssignment, Exam, ...

Ask yourself: What class changes during planning? Which class has variables that I want the Solver to change for me? That class is a planning entity. Most use cases have only 1 planning entity class.

Note

In real-time planning, problem facts can change during planning, because the problem itself changes. However, that doesn't make them planning entities.

A good model can greatly improve the success of your planning implementation. For inspiration, take a look at how the examples modeled their domain:

When in doubt, it's usually the many side of a many to one relationship that is the planning entity. For example in employee rostering, the planning entity class is ShiftAssignment, not Employee. Vehicle routing is special, because it uses a chained planning variable.

In OptaPlanner all problems facts and planning entities are plain old JavaBeans (POJO's). You can load them from a database (JDBC/JPA/JDO), an XML file, a data repository, a noSQL cloud, ...: OptaPlanner doesn't care.

4.3.2. Problem fact

A problem fact is any JavaBean (POJO) with getters that does not change during planning. Implementing the interface Serializable is recommended (but not required). For example in n queens, the columns and rows are problem facts:

public class Column implements Serializable {


    private int index;


    // ... getters

}

public class Row implements Serializable {


    private int index;


    // ... getters

}

A problem fact can reference other problem facts of course:

public class Course implements Serializable {


    private String code;


    private Teacher teacher; // Other problem fact

    private int lectureSize;

    private int minWorkingDaySize;


    private List<Curriculum> curriculumList; // Other problem facts

    private int studentSize;


    // ... getters

}

A problem fact class does not require any Planner specific code. For example, you can reuse your domain classes, which might have JPA annotations.

Note

Generally, better designed domain classes lead to simpler and more efficient score constraints. Therefore, when dealing with a messy legacy system, it can sometimes be worth it to convert the messy domain set into a planner specific POJO set first. For example: if your domain model has 2 Teacher instances for the same teacher that teaches at 2 different departments, it's hard to write a correct score constraint that constrains a teacher's spare time.

Alternatively, you can sometimes also introduce a cached problem fact to enrich the domain model for planning only.

4.3.3. Planning entity

4.3.3.1. Planning entity annotation

A planning entity is a JavaBean (POJO) that changes during solving, for example a Queen that changes to another row. A planning problem has multiple planning entities, for example for a single n queens problem, each Queen is a planning entity. But there's usually only 1 planning entity class, for example the Queen class.

A planning entity class needs to be annotated with the @PlanningEntity annotation.

Each planning entity class has 1 or more planning variables. It usually also has 1 or more defining properties. For example in n queens, a Queen is defined by its Column and has a planning variable Row. This means that a Queen's column never changes during solving, while its row does change.

@PlanningEntity

public class Queen {


    private Column column;


    // Planning variables: changes during planning, between score calculations.

    private Row row;


    // ... getters and setters

}

A planning entity class can have multiple planning variables. For example, a Lecture is defined by its Course and its index in that course (because 1 course has multiple lectures). Each Lecture needs to be scheduled into a Period and a Room so it has 2 planning variables (period and room). For example: the course Mathematics has 8 lectures per week, of which the first lecture is Monday morning at 08:00 in room 212.

@PlanningEntity

public class Lecture {


    private Course course;

    private int lectureIndexInCourse;


    // Planning variables: changes during planning, between score calculations.

    private Period period;

    private Room room;


    // ...

}

The solver configuration also needs to be made aware of each planning entity class:

<solver>

  ...

  <entityClass>org.optaplanner.examples.nqueens.domain.Queen</entityClass>

  ...

</solver>

Some uses cases have multiple planning entity classes. For example: route freight and trains into railway network arcs, where each freight can use multiple trains over its journey and each train can carry multiple freights per arc. Having multiple planning entity classes directly raises the implementation complexity of your use case.

Note

Do not create unnecessary planning entity classes. This leads to difficult Move implementations and slower score calculation.

For example, do not create a planning entity class to hold the total free time of a teacher, which needs to be kept up to date as the Lecture planning entities change. Instead, calculate the free time in the score constraints and put the result per teacher into a logically inserted score object.

If historic data needs to be considered too, then create problem fact to hold the total of the historic assignments up to, but not including, the planning window (so it doesn't change when a planning entity changes) and let the score constraints take it into account.

4.3.3.2. Planning entity difficulty

Some optimization algorithms work more efficiently if they have an estimation of which planning entities are more difficult to plan. For example: in bin packing bigger items are harder to fit, in course scheduling lectures with more students are more difficult to schedule and in n queens the middle queens are more difficult to fit on the board.

Therefore, you can set a difficultyComparatorClass to the @PlanningEntity annotation:

@PlanningEntity(difficultyComparatorClass = CloudProcessDifficultyComparator.class)

public class CloudProcess {

    // ...

}

public class CloudProcessDifficultyComparator implements Comparator<CloudProcess> {


    public int compare(CloudProcess a, CloudProcess b) {

        return new CompareToBuilder()

                .append(a.getRequiredMultiplicand(), b.getRequiredMultiplicand())

                .append(a.getId(), b.getId())

                .toComparison();

    }


}

Alternatively, you can also set a difficultyWeightFactoryClass to the @PlanningEntity annotation, so you have access to the rest of the problem facts from the Solution too:

@PlanningEntity(difficultyWeightFactoryClass = QueenDifficultyWeightFactory.class)

public class Queen {

    // ...

}

See sorted selection for more information.

Important

Difficulty should be implemented ascending: easy entities are lower, difficult entities are higher. For example in bin packing: small item < medium item < big item.

Even though some algorithms start with the more difficult entities first, they just reverse the ordering.

None of the current planning variable state should be used to compare planning entity difficult. During Construction Heuristics, those variables are likely to be null anyway. For example, a Queen's row variable should not be used.

4.3.4. Planning variable

4.3.4.1. Planning variable annotation

A planning variable is a property (including getter and setter) on a planning entity. It points to a planning value, which changes during planning. For example, a Queen's row property is a planning variable. Note that even though a Queen's row property changes to another Row during planning, no Row instance itself is changed.

A planning variable getter needs to be annotated with the @PlanningVariable annotation, which needs a non-empty valueRangeProviderRefs property.

@PlanningEntity

public class Queen {


    private Row row;


    // ...


    @PlanningVariable(valueRangeProviderRefs = {"rowRange"})

    public Row getRow() {

        return row;

    }


    public void setRow(Row row) {

        this.row = row;

    }


}

The valueRangeProviderRefs property defines what are the possible planning values for this planning variable. It references 1 or more @ValueRangeProvider id's.

4.3.4.2. Nullable planning variable

By default, an initialized planning variable cannot be null, so an initialized solution will never use null for any of its planning variables. In an over-constrained use case, this can be contra productive. For example: in task assignment with too many tasks for the workforce, we would rather leave low priority tasks unassigned instead of assigning them to an overloaded worker.

To allow an initialized planning variable to be null, set nullable to true:

    @PlanningVariable(..., nullable = true)

    public Worker getWorker() {

        return worker;

    }

Important

Planner will automatically add the value null to the value range. There is no need to add null in a collection used by a ValueRangeProvider.

Note

Using a nullable planning variable implies that your score calculation is responsible for punishing (or even rewarding) variables with a null value.

Repeated planning (especially real-time planning) does not mix well with a nullable planning variable: every time the Solver starts or a problem fact change is made, the construction heuristics will try to initialize all the null variables again, which can be a huge waste of time. One way to deal with this, is to change when a planning entity should be reinitialized with an reinitializeVariableEntityFilter:

    @PlanningVariable(..., nullable = true, reinitializeVariableEntityFilter = ReinitializeTaskFilter.class)

    public Worker getWorker() {

        return worker;

    }

4.3.4.3. When is a planning variable considered initialized?

A planning variable is considered initialized if its value is not null or if the variable is nullable. So a nullable variable is always considered initialized, even when a custom reinitializeVariableEntityFilter triggers a reinitialization during construction heuristics.

A planning entity is initialized if all of its planning variables are initialized.

A Solution is initialized if all of its planning entities are initialized.

4.3.5. Planning value and planning value ranges

4.3.5.1. Planning value

A planning value is a possible value for a planning variable. Usually, a planning value is a problem fact, but it can also be any object, for example a double. It can even be another planning entity or even a interface implemented by both a planning entity and a problem fact.

A planning value range is the set of possible planning values for a planning variable. This set can be a countable (for example row 1, 2, 3 or 4) or uncountable (for example any double between 0.0 and 1.0).

4.3.5.2. Planning value range provider

4.3.5.2.1. Introduction

The value range of a planning variable is defined with the @ValueRangeProvider annotation. A @ValueRangeProvider annotation always has a property id, which is referenced by the @PlanningVariable's property valueRangeProviderRefs.

This annotation can be located on 2 types of methods:

On the Solution: All planning entities share the same value range.
On the planning entity: The value range differs per planning entity. This is less common.

The return type of that method can be 2 types:

Collection: The value range is defined by a Collection (usually a List) of it's possible values.
ValueRange: The value range is defined by its bounds. This is less common.

4.3.5.2.2. `ValueRangeProvider` on the `Solution`

All instances of the same planning entity class share the same set of possible planning values for that planning variable. This is the most common way to configure a value range.

The Solution implementation has method which returns a Collection (or a ValueRange). Any value from that Collection is a possible planning value for this planning variable.

    @PlanningVariable(valueRangeProviderRefs = {"rowRange"})

    public Row getRow() {

        return row;

    }

@PlanningSolution

public class NQueens implements Solution<SimpleScore> {


    // ...


    @ValueRangeProvider(id = "rowRange")

    public List<Row> getRowList() {

        return rowList;

    }


}

Important

That Collection (or ValueRange) must not contain the value null, not even for a nullable planning variable.

4.3.5.2.3. `ValueRangeProvider` on the planning entity

Each planning entity has its own set of possible planning values for a planning variable. For example, if a teacher can never teach in a room that does not belong to his department, lectures of that teacher can limit their room value range to the rooms of his department.

    @PlanningVariable(valueRangeProviderRefs = {"possibleRoomRange"})

    public Room getRoom() {

        return room;

    }


    @ValueRangeProvider(id = "possibleRoomRange")

    public List<Room> getPossibleRoomList() {

        return getCourse().getTeacher().getPossibleRoomList();

    }

Never use this to enforce a soft constraint (or even a hard constraint when the problem might not have a feasible solution). For example: Unless there is no other way, a teacher can not teach in a room that does not belong to his department. In this case, the teacher should not be limited in his room value range (because sometimes there is no other way).

Note

By limiting the value range specifically of 1 planning entity, you are effectively creating a build-in hard constraint. This can be a very good thing, as the number of possible solutions is severely lowered. But this can also be a bad thing because it takes away the freedom of the optimization algorithms to temporarily break that constraint in order to escape a local optima.

A planning entity should not use other planning entities to determinate its value range. That would only try to make it solve the planning problem itself and interfere with the optimization algorithms.

Warning

A value range on planning entity is not (yet) compatible with a chained variable, nor with generic swap moves.

4.3.5.2.4. `ValueRangeFactory`

Instead of a Collection, you can also return a ValueRange or CountableValueRange, build by the ValueRangeFactory:

    @ValueRangeProvider(id = "delayRange")

    public CountableValueRange<Integer> getDelayRange() {

        return ValueRangeFactory.createIntValueRange(0, 5000);

    }

A ValueRange uses far less memory, because it only holds the bounds. In the example above, a Collection would need to hold all 5000 ints, instead of just the 2 bounds.

Furthermore, an incrementUnit can be specified, for example if you have to buy stocks in units of 200 pieces:

    @ValueRangeProvider(id = "stockAmountRange")

    public CountableValueRange<Integer> getStockAmountRange() {

         // Range: 0, 200, 400, 600, ..., 9999600, 9999800, 10000000

        return ValueRangeFactory.createIntValueRange(0, 10000000, 200);

    }

Note

Return CountableValueRange instead of ValueRange whenever possible (so OptaPlanner knows it's countable).

The ValueRangeFactory supports several value class types:

int: An integer range.
double: A floating point range which only supports random selection (because it does not implement CountableValueRange).
BigDecimal: A decimal point range. By default, the increment unit is the lowest non-zero value in the scale of the bounds.

4.3.5.2.5. Combining ValueRangeProviders

Value range providers can be combined, for example:

    @PlanningVariable(valueRangeProviderRefs = {"companyCarRange", "personalCarRange"})

    public Car getCar() {

        return car;

    }

    @ValueRangeProvider(id = "companyCarRange")

    public List<CompanyCar> getCompanyCarList() {

        return companyCarList;

    }


    @ValueRangeProvider(id = "personalCarRange")

    public List<PersonalCar> getPersonalCarList() {

        return personalCarList;

    }

4.3.5.3. Planning value strength

Some optimization algorithms work more efficiently if they have an estimation of which planning values are stronger, which means they are more likely to satisfy a planning entity. For example: in bin packing bigger containers are more likely to fit an item and in course scheduling bigger rooms are less likely to break the student capacity constraint.

Therefore, you can set a strengthComparatorClass to the @PlanningVariable annotation:

    @PlanningVariable(..., strengthComparatorClass = CloudComputerStrengthComparator.class)

    public CloudComputer getComputer() {

        // ...

    }

public class CloudComputerStrengthComparator implements Comparator<CloudComputer> {


    public int compare(CloudComputer a, CloudComputer b) {

        return new CompareToBuilder()

                .append(a.getMultiplicand(), b.getMultiplicand())

                .append(b.getCost(), a.getCost()) // Descending (but this is debatable)

                .append(a.getId(), b.getId())

                .toComparison();

    }


}

Note

If you have multiple planning value classes in the same value range, the strengthComparatorClass needs to implement a Comparator of a common superclass (for example Comparator<Object>) and be able to handle comparing instances of those different classes.

Alternatively, you can also set a strengthWeightFactoryClass to the @PlanningVariable annotation, so you have access to the rest of the problem facts from the solution too:

    @PlanningVariable(..., strengthWeightFactoryClass = RowStrengthWeightFactory.class)

    public Row getRow() {

        // ...

    }

See sorted selection for more information.

Important

Strength should be implemented ascending: weaker values are lower, stronger values are higher. For example in bin packing: small container < medium container < big container.

None of the current planning variable state in any of the planning entities should be used to compare planning values. During construction heuristics, those variables are likely to be null anyway. For example, none of the row variables of any Queen may be used to determine the strength of a Row.

4.3.5.4. Chained planning variable (TSP, VRP, ...)

Some use cases, such as TSP and Vehicle Routing, require chaining. This means the planning entities point to each other and form a chain. By modeling the problem as a set of chains (instead of a set of trees/loops), the search space is heavily reduced.

A planning variable that is chained either:

Directly points to a planning fact, which is called an anchor.
Points to another planning entity with the same planning variable, which recursively points to an anchor.

Here are some example of valid and invalid chains:

Every initialized planning entity is part of an open-ended chain that begins from an anchor. A valid model means that:

A chain is never a loop. The tail is always open.
Every chain always has exactly 1 anchor. The anchor is a problem fact, never a planning entity.
A chain is never a tree, it is always a line. Every anchor or planning entity has at most 1 trailing planning entity.
Every initialized planning entity is part of a chain.
An anchor with no planning entities pointing to it, is also considered a chain.

Warning

A planning problem instance given to the Solver must be valid.

Note

If your constraints dictate a closed chain, model it as an open-ended chain (which is easier to persist in a database) and implement a score constraint for the last entity back to the anchor.

The optimization algorithms and build-in Move's do chain correction to guarantee that the model stays valid:

Warning

A custom Move implementation must leave the model in a valid state.

For example, in TSP the anchor is a Domicile (in vehicle routing it is Vehicle):

public class Domicile ... implements Standstill {


    ...


    public City getCity() {...}


}

The anchor (which is a problem fact) and the planning entity implement a common interface, for example TSP's Standstill:

public interface Standstill {


    City getCity();


}

That interface is the return type of the planning variable. Furthermore, the planning variable is chained. For example TSP's Visit (in vehicle routing it is Customer):

@PlanningEntity

public class Visit ... implements Standstill {


    ...


    public City getCity() {...}


    @PlanningVariable(graphType = PlanningVariableGraphType.CHAINED, valueRangeProviderRefs = {"domicileRange", "visitRange"})

    public Standstill getPreviousStandstill() {

        return previousStandstill;

    }


    public void setPreviousStandstill(Standstill previousStandstill) {

        this.previousStandstill = previousStandstill;

    }


}

Notice how 2 value range providers are usually combined:

The value range provider which holds the anchors, for example domicileList.
The value range provider which holds the initialized planning entities, for example visitList.

4.3.5.5. Bi-directional variable

2 variables are bi-directional if their instances always point to each other (unless they point to null). So if A references B, then B references A.

To map a bi-directional relationship between 2 planning variables, annotate the master side as a normal (= genuine) planning variable:

@PlanningEntity

public class Customer ... {


    @PlanningVariable(graphType = PlanningVariableGraphType.CHAINED, ...)

    public Standstill getPreviousStandstill() {

        return previousStandstill;

    }


    public void setPreviousStandstill(Standstill previousStandstill) {...}


}

And then annotate the other side as a @InverseRelationShadowVariable annotation.

@PlanningEntity

public interface Standstill {


    @InverseRelationShadowVariable(sourceVariableName = "previousStandstill")

    Customer getNextCustomer();

    void setNextCustomer(Customer nextCustomer);


}

The sourceVariableName property is the name of the planning variable on the return type of the getter.

The @InverseRelationShadowVariable variable is a form of a shadow variable: Planner uses a build-in VariableListener to update its state.

4.3.5.6. Variable listener that updates shadow variables

A shadow variable is a variables who's correct value can be deduced from the state of the genuine planning variables. Even though such a variable violates the principle of normalization by definition, in some use cases it can be very practical to use a shadow variable, especially to express the constraints more naturally. For example in vehicle routing with time windows: the arrival time at a customer for a vehicle can be calculated based on the previously visited customers of that vehicle (and the known travel times between 2 locations).

As the customers for a vehicle change, the arrival time is automatically adjusted. For more information, see the vehicle routing domain model.

To use custom VariableListener, implement the interface and annotate it on the shadow variable(s) that needs to change.

    @PlanningVariable(...)

    public Standstill getPreviousStandstill() {

        return previousStandstill;

    }


    @CustomShadowVariable(variableListenerClass = VehicleUpdatingVariableListener.class,

            sources = {@CustomShadowVariable.Source(variableName = "previousStandstill")})

    public Vehicle getVehicle() {

        return vehicle;

    }

The variableName is the variable that triggers changes in the shadow variable(s).

Note

If the class of the trigger variable is different than the shadow variable, also specify the entityClass on @CustomShadowVariable.Source. In that case, make sure that that entityClass is also properly configured as a planning entity class in the solver config, or the VariableListener will simply never trigger.

Any class that has a shadow variable, is a planning entity class, even it has no genuine planning variables.

For example, the VehicleUpdatingVariableListener assures that every Customer in a chain has the same Vehicle, namely the chain's anchor.

public class VehicleUpdatingVariableListener implements VariableListener<Customer> {


    public void afterEntityAdded(ScoreDirector scoreDirector, Customer customer) {

        updateVehicle(scoreDirector, customer);

    }


    public void afterVariableChanged(ScoreDirector scoreDirector, Customer customer) {

        updateVehicle(scoreDirector, customer);

    }


    ...


    protected void updateVehicle(ScoreDirector scoreDirector, Customer sourceCustomer) {

        Standstill previousStandstill = sourceCustomer.getPreviousStandstill();

        Vehicle vehicle = previousStandstill == null ? null : previousStandstill.getVehicle();

        Customer shadowCustomer = sourceCustomer;

        while (shadowCustomer != null && shadowCustomer.getVehicle() != vehicle) {

            scoreDirector.beforeVariableChanged(shadowCustomer, "vehicle");

            shadowCustomer.setVehicle(vehicle);

            scoreDirector.afterVariableChanged(shadowCustomer, "vehicle");

            shadowCustomer = shadowCustomer.getNextCustomer();

        }

    }


}

Warning

A VariableListener can only change shadow variables. It must never change a genuine planning variable or a problem fact.

Warning

Any change of a shadow variable must be told to the ScoreDirector.

From a score calculation perspective, a shadow variable is like any other planning variable. From an optimization perspective, Planner effectively only optimizes the genuine variables (and mostly ignores the shadow variables): it just assures that when a genuine variable changes, any dependent shadow variables are changed accordingly.

4.3.6. Planning problem and planning solution

4.3.6.1. Planning problem instance

A dataset for a planning problem needs to be wrapped in a class for the Solver to solve. You must implement this class. For example in n queens, this in the NQueens class which contains a Column list, a Row list and a Queen list.

A planning problem is actually a unsolved planning solution or - stated differently - an uninitialized Solution. Therefor, that wrapping class must implement the Solution interface. For example in n queens, that NQueens class implements Solution, yet every Queen in a fresh NQueens class is not yet assigned to a Row (their row property is null). So it's not a feasible solution. It's not even a possible solution. It's an uninitialized solution.

4.3.6.2. `Solution` interface

You need to present the problem as a Solution instance to the Solver. So you need to have a class that implements the Solution interface:

public interface Solution<S extends Score> {


    S getScore();

    void setScore(S score);


    Collection<? extends Object> getProblemFacts();


}

For example, an NQueens instance holds a list of all columns, all rows and all Queen instances:

public class NQueens implements Solution<SimpleScore> {


    private int n;


    // Problem facts

    private List<Column> columnList;

    private List<Row> rowList;


    // Planning entities

    private List<Queen> queenList;


    // ...

}

4.3.6.3. The `getScore()` and `setScore()` methods

A Solution requires a score property. The score property is null if the Solution is uninitialized or if the score has not yet been (re)calculated. The score property is usually typed to the specific Score implementation you use. For example, NQueens uses a SimpleScore:

public class NQueens implements Solution<SimpleScore> {


    private SimpleScore score;


    public SimpleScore getScore() {

        return score;

    }


    public void setScore(SimpleScore score) {

        this.score = score;

    }


    // ...

}

Most use cases use a HardSoftScore instead:

public class CourseSchedule implements Solution<HardSoftScore> {


    private HardSoftScore score;


    public HardSoftScore getScore() {

        return score;

    }


    public void setScore(HardSoftScore score) {

        this.score = score;

    }


    // ...

}

See the Score calculation section for more information on the Score implementations.

4.3.6.4. The `getProblemFacts()` method

The method is only used if Drools is used for score calculation. Other score directors do not use it.

All objects returned by the getProblemFacts() method will be asserted into the Drools working memory, so the score rules can access them. For example, NQueens just returns all Column and Row instances.

    public Collection<? extends Object> getProblemFacts() {

        List<Object> facts = new ArrayList<Object>();

        facts.addAll(columnList);

        facts.addAll(rowList);

        // Do not add the planning entity's (queenList) because that will be done automatically

        return facts;

    }

All planning entities are automatically inserted into the Drools working memory. Do not add them in the method getProblemFacts().

Note

A common mistake is to use facts.add(...) instead of fact.addAll(...) for a Collection, which leads to score rules failing to match because the elements of that Collection aren't in the Drools working memory.

The method getProblemFacts() is not called much: at most only once per solver phase per solver thread.

4.3.6.5. Cached problem fact

A cached problem fact is a problem fact that doesn't exist in the real domain model, but is calculated before the Solver really starts solving. The method getProblemFacts() has the chance to enrich the domain model with such cached problem facts, which can lead to simpler and faster score constraints.

For example in examination, a cached problem fact TopicConflict is created for every 2 Topic's which share at least 1 Student.

    public Collection<? extends Object> getProblemFacts() {

        List<Object> facts = new ArrayList<Object>();

        // ...

        facts.addAll(calculateTopicConflictList());

        // ...

        return facts;

    }


    private List<TopicConflict> calculateTopicConflictList() {

        List<TopicConflict> topicConflictList = new ArrayList<TopicConflict>();

        for (Topic leftTopic : topicList) {

            for (Topic rightTopic : topicList) {

                if (leftTopic.getId() < rightTopic.getId()) {

                    int studentSize = 0;

                    for (Student student : leftTopic.getStudentList()) {

                        if (rightTopic.getStudentList().contains(student)) {

                            studentSize++;

                        }

                    }

                    if (studentSize > 0) {

                        topicConflictList.add(new TopicConflict(leftTopic, rightTopic, studentSize));

                    }

                }

            }

        }

        return topicConflictList;

    }

Any score constraint that needs to check if no 2 exams have a topic which share a student are being scheduled close together (depending on the constraint: at the same time, in a row or in the same day), can simply use the TopicConflict instance as a problem fact, instead of having to combine every 2 Student instances.

4.3.6.6. Cloning a `Solution`

Most (if not all) optimization algorithms clone the solution each time they encounter a new best solution (so they can recall it later) or to work with multiple solutions in parallel.

Note

There are many ways to clone, such as a shallow clone, deep clone, ... This context focuses on a planning clone.

A planning clone of a Solution must fulfill these requirements:

The clone must represent the same planning problem. Usually it reuses the same instances of the problem facts and problem fact collections as the original.
The clone must use different, cloned instances of the entities and entity collections. Changes to an original Solution's entity's variables must not effect its clone.

Implementing a planning clone method is hard, therefore you don't need to implement it.

4.3.6.6.1. `FieldAccessingSolutionCloner`

This SolutionCloner is used by default. It works for the majority of use cases.

Warning

When the FieldAccessingSolutionCloner clones your entity collection, it might not recognize the implementation and replace it with ArrayList, LinkedHashSet or TreeSet (whichever is more applicable). It recognizes most of the common JDK Collection implementations.

The FieldAccessingSolutionCloner does not clone problem facts by default. If any of your problem facts needs to be deep cloned for a planning clone, for example if the problem fact references a planning entity or the planning solution, mark it with a @DeepPlanningClone annotation:

@DeepPlanningClone

public class SeatDesignationDependency {

    private SeatDesignation leftSeatDesignation; // planning entity

    private SeatDesignation rightSeatDesignation; // planning entity

    ...

}

In the example above, because SeatDesignation is a planning entity (which is deep planning cloned automatically), SeatDesignationDependency must be deep planning cloned too.

Alternatively, the @DeepPlanningClone annotation can also be used on a getter method.

4.3.6.6.2. Custom cloning: Make `Solution` implement `PlanningCloneable`

If your Solution implements PlanningCloneable, Planner will automatically choose to clone it by calling the method planningClone().

public interface PlanningCloneable<T> {


    T planningClone();


}

For example: If NQueens implements PlanningCloneable, it would only deep clone all Queen instances. When the original solution is changed during planning, by changing a Queen, the clone stays the same.

public class NQueens implements Solution<...>, PlanningCloneable<NQueens> {

    ...


    /**

     * Clone will only deep copy the {@link #queenList}.

     */

    public NQueens planningClone() {

        NQueens clone = new NQueens();

        clone.id = id;

        clone.n = n;

        clone.columnList = columnList;

        clone.rowList = rowList;

        List<Queen> clonedQueenList = new ArrayList<Queen>(queenList.size());

        for (Queen queen : queenList) {

            clonedQueenList.add(queen.planningClone());

        }

        clone.queenList = clonedQueenList;

        clone.score = score;

        return clone;

    }

}

The planningClone() method should only deep clone the planning entities. Notice that the problem facts, such as Column and Row are normally not cloned: even their List instances are not cloned. If you were to clone the problem facts too, then you'd have to make sure that the new planning entity clones also refer to the new problem facts clones used by the solution. For example, if you would clone all Row instances, then each Queen clone and the NQueens clone itself should refer to those new Row clones.

Warning

Cloning an entity with a chained variable is devious: a variable of an entity A might point to another entity B. If A is cloned, then it's variable must point to the clone of B, not the original B.

4.3.6.7. Build an uninitialized solution

Build a Solution instance to represent your planning problem, so you can set it on the Solver as the planning problem to solve. For example in n queens, an NQueens instance is created with the required Column and Row instances and every Queen set to a different column and every row set to null.

    private NQueens createNQueens(int n) {

        NQueens nQueens = new NQueens();

        nQueens.setId(0L);

        nQueens.setN(n);

        nQueens.setColumnList(createColumnList(nQueens));

        nQueens.setRowList(createRowList(nQueens));

        nQueens.setQueenList(createQueenList(nQueens));

        return nQueens;

    }


    private List<Queen> createQueenList(NQueens nQueens) {

        int n = nQueens.getN();

        List<Queen> queenList = new ArrayList<Queen>(n);

        long id = 0;

        for (Column column : nQueens.getColumnList()) {

            Queen queen = new Queen();

            queen.setId(id);

            id++;

            queen.setColumn(column);

            // Notice that we leave the PlanningVariable properties on null

            queenList.add(queen);

        }

        return queenList;

    }

Figure 4.1. Uninitialized solution for the 4 queens puzzle

Usually, most of this data comes from your data layer, and your Solution implementation just aggregates that data and creates the uninitialized planning entity instances to plan:

        private void createLectureList(CourseSchedule schedule) {

            List<Course> courseList = schedule.getCourseList();

            List<Lecture> lectureList = new ArrayList<Lecture>(courseList.size());

            for (Course course : courseList) {

                for (int i = 0; i < course.getLectureSize(); i++) {

                    Lecture lecture = new Lecture();

                    lecture.setCourse(course);

                    lecture.setLectureIndexInCourse(i);

                    // Notice that we leave the PlanningVariable properties (period and room) on null

                    lectureList.add(lecture);

                }

            }

            schedule.setLectureList(lectureList);

        }

4.4. Use the `Solver`

4.4.1. The Solver interface

A Solver implementation will solve your planning problem.

public interface Solver {

    

    void solve(Solution planningProblem);


    Solution getBestSolution();


    // ...


}

A Solver can only solve 1 planning problem instance at a time. A Solver should only be accessed from a single thread, except for the methods that are specifically javadocced as being thread-safe. It's build with a SolverFactory, do not implement or build it yourself.

4.4.2. Solving a problem

Solving a problem is quite easy once you have:

A Solver build from a solver configuration
A Solution that represents the planning problem instance

Just set the planning problem, solve it and extract the best solution:

    solver.solve(planningProblem);

    Solution bestSolution = solver.getBestSolution();

For example in n queens, the method getBestSolution() will return an NQueens instance with every Queen assigned to a Row.

Figure 4.2. Best solution for the 4 queens puzzle in 8 ms (also an optimal solution)

The solve(Solution) method can take a long time (depending on the problem size and the solver configuration). The Solver will remember (actually clone) the best solution it encounters during its solving. Depending on a number factors (including problem size, how much time the Solver has, the solver configuration, ...), that best solution will be a feasible or even an optimal solution.

Note

The Solution instance given to the method solve(Solution) will be changed by the Solver, but it do not mistake it for the best solution.

The Solution instance returned by the method getBestSolution() will most likely be a clone of the instance given to the method solve(Solution), which means it's a different instance.

Note

The Solution instance given to the method solve(Solution) does not need to be uninitialized. It can be partially or fully initialized, which is likely to be the case in repeated planning.

4.4.3. Environment mode: Are there bugs in my code?

The environment mode allows you to detect common bugs in your implementation. It does not affect the logging level.

You can set the environment mode in the solver configuration XML file:


<solver>

  <environmentMode>FAST_ASSERT</environmentMode>

  ...

</solver>

A solver has a single Random instance. Some solver configurations use the Random instance a lot more than others. For example simulated annealing depends highly on random numbers, while tabu search only depends on it to deal with score ties. The environment mode influences the seed of that Random instance.

There are 4 environment modes:

Note

Despite the reproducible mode, your application might still not be fully reproducible because of:

Use of HashSet (or another Collection which has an inconsistent order between JVM runs) for collections of planning entities or planning values (but not normal problem facts), especially in the Solution implementation. Replace it with LinkedHashSet.
Combining a time gradient dependent algorithms (most notably Simulated Annealing) together with time spent termination. A sufficiently large difference in allocated CPU time will influence the time gradient values. Replace Simulated Annealing with Late Acceptance. Or instead, replace time spent termination with step count termination.

The reproducible mode is slightly slower than the production mode. If your production environment requires reproducibility, use this mode in production too.

In practice, this mode uses the default, fixed random seed if no seed is specified, and it also disables certain concurrency optimizations (such as work stealing).

4.4.3.5. PRODUCTION

The production mode is the fastest, but it is not reproducible. It is recommended for a production environment, unless reproducibility is required.

In pratice, this mode uses no fixed random seed if no seed is specified.

4.4.4. Logging level: What is the `Solver` doing?

The best way to illuminate the black box that is a Solver, is to play with the logging level:

error: Log errors, except those that are thrown to the calling code as a RuntimeException.
Note
If an error happens, Planner normally fails fast: it throws a subclass of RuntimeException with a detailed message to the calling code. It does not log it as an error itself to avoid duplicate log messages. Except if the calling code explicitly catches and eats that RuntimeException, a Thread's default ExceptionHandler will log it as an error anyway. Meanwhile, the code is disrupted from doing further harm or obfuscating the error.
warn: Log suspicious circumstances.
info: Log every phase and the solver itself. See scope overview.
debug: Log every step of every phase. See scope overview.
trace: Log every move of every step of every phase. See scope overview.
Note
Turning on trace logging, will slow down performance considerably: it's often 4 times slower. However, it's invaluable during development to discover a bottleneck.
Even debug logging can slow down performance considerably for fast stepping algorithms (such as Late Acceptance and Simulated Annealing), but not for slow stepping algorithms (such as Tabu Search).

For example, set it to debug logging, to see when the phases end and how fast steps are taken:

INFO  Solving started: time spent (3), best score (uninitialized/0), random (JDK with seed 0).
DEBUG     CH step (0), time spent (5), score (0), selected move count (1), picked move (col2@null => row0).
DEBUG     CH step (1), time spent (7), score (0), selected move count (3), picked move (col1@null => row2).
DEBUG     CH step (2), time spent (10), score (0), selected move count (4), picked move (col3@null => row3).
DEBUG     CH step (3), time spent (12), score (-1), selected move count (4), picked move (col0@null => row1).
INFO  Construction Heuristic phase (0) ended: step total (4), time spent (12), best score (-1).
DEBUG     LS step (0), time spent (19), score (-1),     best score (-1), accepted/selected move count (12/12), picked move (col1@row2 => row3).
DEBUG     LS step (1), time spent (24), score (0), new best score (0), accepted/selected move count (9/12), picked move (col3@row3 => row2).
INFO  Local Search phase (1) ended: step total (2), time spent (24), best score (0).
INFO  Solving ended: time spent (24), best score (0), average calculate count per second (1625).

All time spent values are in milliseconds.

Everything is logged to SLF4J, which is a simple logging facade which delegates every log message to Logback, Apache Commons Logging, Log4j or java.util.logging. Add a dependency to the logging adaptor for your logging framework of choice.

If you're not using any logging framework yet, use Logback by adding this Maven dependency (there is no need to add an extra bridge dependency):


    <dependency>

      <groupId>ch.qos.logback</groupId>

      <artifactId>logback-classic</artifactId>

      <version>1.x</version>

    </dependency>

Configure the logging level on the package org.optaplanner in your logback.xml file:


<configuration>



  <logger name="org.optaplanner" level="debug"/>



  ...



<configuration>

If instead, you're still using Log4J 1.x (and you don't want to switch to its faster successor, Logback), add the bridge dependency:


    <dependency>

      <groupId>org.slf4j</groupId>

      <artifactId>slf4j-log4j12</artifactId>

      <version>1.x</version>

    </dependency>

And configure the logging level on the package org.optaplanner in your log4j.xml file:


<log4j:configuration xmlns:log4j="http://jakarta.apache.org/log4j/">



  <category name="org.optaplanner">

    <priority value="debug" />

  </category>



  ...



</log4j:configuration>

Note

In a multitenant application, multiple Solver instances might be running at the same time. To separate their logging into distinct files, surround the solve() call with an MDC:

        MDC.put("tenant.name",tenantName);

        solver.solve(planningProblem);

        Solution bestSolution = solver.getBestSolution();

        MDC.remove("tenant.name");

Then configure your logger to use different files for each ${tenant.name}. For example in Logback, use a SiftingAppender in logback.xml:


  <appender name="fileAppender" class="ch.qos.logback.classic.sift.SiftingAppender">

    <discriminator>

      <key>tenant.name</key>

      <defaultValue>unknown</defaultValue>

    </discriminator>

    <sift>

      <appender name="fileAppender.${tenant.name}" class="...FileAppender">

        <file>local/log/optaplanner-${tenant.name}.log</file>

        ...

      </appender>

    </sift>

  </appender>

4.4.5. Random number generator

Many heuristics and metaheuristics depend on a pseudorandom number generator for move selection, to resolve score ties, probability based move acceptance, ... During solving, the same Random instance is reused to improve reproducibility, performance and uniform distribution of random values.

To change the random seed of that Random instance, specify a randomSeed:


<solver>

  <randomSeed>0</randomSeed>

  ...

</solver>

To change the pseudorandom number generator implementation, specify a randomType:


<solver>

  <randomType>MERSENNE_TWISTER</randomType>

  ...

</solver>

The following types are supported:

JDK (default): Standard implementation (java.util.Random).
MERSENNE_TWISTER: Implementation by Commons Math.
WELL512A, WELL1024A, WELL19937A, WELL19937C, WELL44497A and WELL44497B: Implementation by Commons Math.

For most use cases, the randomType has no significant impact on the average quality of the best solution on multiple datasets. If you want to confirm this on your use case, use the benchmarker.

Chapter 5. Score calculation

5.1. Score terminology

5.1.1. What is a score?
5.1.2. Score constraint signum (positive or negative)
5.1.3. Score constraint weight
5.1.4. Score level
5.1.5. Pareto scoring (AKA multi-objective optimization scoring)
5.1.6. Combining score techniques
5.1.7. Score interface
5.1.8. Avoid floating point numbers in score calculation

5.2. Choose a Score definition

5.2.1. SimpleScore
5.2.2. HardSoftScore (recommended)
5.2.3. HardMediumSoftScore
5.2.4. BendableScore
5.2.5. Implementing a custom Score

5.3. Calculate the Score

5.3.1. Score calculation types
5.3.2. Easy Java score calculation
5.3.3. Incremental Java score calculation
5.3.4. Drools score calculation
5.3.5. InitializingScoreTrend
5.3.6. Invalid score detection

5.4. Score calculation performance tricks

5.4.1. Overview
5.4.2. Average calculation count per second
5.4.3. Incremental score calculation (with delta's)
5.4.4. Avoid calling remote services during score calculation
5.4.5. Pointless constraints
5.4.6. Build-in hard constraint
5.4.7. Other performance tricks
5.4.8. Score trap
5.4.9. stepLimit benchmark
5.4.10. Fairness score constraints

5.5. Explaining the score: using score calculation outside the Solver

5.1. Score terminology

5.1.1. What is a score?

Every initialized Solution has a score. That score is an objective way to compare 2 solutions: the solution with the higher score is better. The Solver aims to find the Solution with the highest Score of all possible solutions. The best solution is the Solution with the highest Score that Solver has encountered during solving, which might be the optimal solution.

Planner cannot automatically know which Solution is best for your business, so you need to tell it how to calculate the score of a given Solution according to your business needs. There are multiple score techniques that you can use and combine:

Maximize or minimize a constraint: score constraint signum (positive or negative)
Put a cost/profit on constraints: score constraint weight
Prioritize constraints: score level
Pareto scoring

5.1.2. Score constraint signum (positive or negative)

All score techniques are based on constraints. Such a constraint can be a simple pattern (such as Maximize the apple harvest in the solution) or a more complex pattern. A positive constraint is a constraint you're trying to maximize. A negative constraint is a constraint you're trying to minimize.

Notice in the image above, that the optimal solution always has the highest score, regardless if the constraints are positive or negative.

Most planning problems have only negative constraints and therefore have a negative score. In that case, the score is usually the sum of the weight of the negative constraints being broken, with a perfect score of 0. This explains why the score of a solution of 4 queens is the negative (and not the positive!) of the number of queen pairs which can attack each other.

Negative and positive constraints can be combined, even in the same score level.

Note

Don't presume your business knows all its score constraints in advance. Expect score constraints to be added or changed after the first releases.

When a constraint activates (because the negative constraint is broken or the positive constraint is fulfilled) on a certain planning entity set, it is called a constraint match.

5.1.3. Score constraint weight

Not all score constraints are equally important. If breaking one constraint is equally bad as breaking another constraint x times, then those 2 constraints have a different weight (but they are in the same score level). For example in vehicle routing, you can make1 "unhappy driver" constraint match count as much as 2 "fuel tank usage" constraint matches:

Score weighting is often used in use cases where you can put a price tag on everything. In that case, the positive constraints maximize revenue and the negative constraints minimize expenses: together they maximize profit. Alternatively, score weighting is also often used to create social fairness. For example: nurses that request a free day on New Year's eve pay a higher weight than on a normal day.

The weight of a constraint match can be dynamically based on the planning entities involved. For example in cloud balance: the weight of the soft constraint match for an active Computer is the cost of that Computer.

5.1.4. Score level

Sometimes a score constraint outranks another score constraint, no matter how many times the other is broken. In that case, those score constraints are in different levels. For example: a nurse cannot do 2 shifts at the same time (due to the constraints of physical reality), this outranks all nurse happiness constraints.

Most use cases have only 2 score levels: hard and soft. When comparing 2 scores, they are compared lexicographically: the first score level gets compared first. If those differ, the others score levels are ignored. For example: a score that breaks 0 hard constraints and 1000000 soft constraints is better than a score that breaks 1 hard constraint and 0 soft constraints.

Score levels often employ score weighting per level. In such case, the hard constraint level usually makes the solution feasible and the soft constraint level maximizes profit by weighting the constraints on price.

Don't use a big constraint weight when your business actually wants different score levels. That hack, known as score folding, is broken:

Note

Your business will probably tell you that your hard constraints all have the same weight, because they cannot be broken (so their weight does not matter). This is not true and it could create a score trap. For example in cloud balance: if a Computer has 7 CPU too little for its Processes, then it must be weighted 7 times as much as if it had only 1 CPU too little. This way, there is an incentive to move a Process with 6 CPU or less away from that Computer.

3 or more score levels is supported. For example: a company might decide that profit outranks employee satisfaction (or visa versa), while both are outranked by the constraints of physical reality.

5.1.5. Pareto scoring (AKA multi-objective optimization scoring)

Far less common is the use case of pareto optimization, which is also known under the more confusing term multi-objective optimization. In pareto scoring, score constraints are in the same score level, yet they are not weighted against each other. When 2 scores are compared, each of the score constraints are compared individually and the score with the most dominating score constraints wins. Pareto scoring can even be combined with score levels and score constraint weighting.

Consider this example with positive constraints, where we want to get the most apples and oranges. Since it's impossible to compare apples and oranges, we can't weight them against each other. Yet, despite that we can't compare them, we can state that 2 apples are better then 1 apple. Similarly, we can state that 2 apples and 1 orange are better than just 1 orange. So despite our inability to compare some Scores conclusively (at which point we declare them equal), we can find a set of optimal scores. Those are called pareto optimal.

Scores are considered equal far more often. It's left up to a human to choose the better out of a set of best solutions (with equal scores) found by Planner. In the example above, the user must choose between solution A (3 apples and 1 orange) and solution B (1 apples and 6 oranges). It's guaranteed that Planner has not found another solution which has more apples or more oranges or even a better combination of both (such as 2 apples and 3 oranges).

To implement pareto scoring in Planner, implement a custom ScoreDefinition and Score (and replace the BestSolutionRecaller). Future versions will provide out-of-the-box support.

Note

A pareto Score's method compareTo is not transitive because it does a pareto comparison. For example: 2 apples is greater than 1 apple. 1 apples is equal to 1 orange. Yet, 2 apples are not greater than 1 orange (but actually equal). Pareto comparison violates the contract of the interface java.lang.Comparable's method compareTo, but Planner's systems are pareto comparison safe, unless explicitly stated otherwise in this documentation.

5.1.6. Combining score techniques

All the score techniques mentioned above, can be combined seamlessly:

5.1.7. `Score` interface

A score is represented by the Score interface, which naturally extends Comparable:

public interface Score<...> extends Comparable<...> {

    ...

}

The Score implementation to use depends on your use case. Your score might not efficiently fit in a single long value. Planner has several build-in Score implementations, but you can implement a custom Score too. Most use cases tend to use the build-in HardSoftScore.

The Score implementation (for example HardSoftScore) must be the same throughout a Solver runtime. The Score implementation is configured in the solver configuration as a ScoreDefinition:


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    ...

  </scoreDirectorFactory>

5.1.8. Avoid floating point numbers in score calculation

Avoid the use of float and double for score calculation. Use BigDecimal instead.

Floating point numbers (float and double) cannot represent a decimal number correctly. For example: a double cannot hold the value 0.05 correctly. Instead, it holds the nearest representable value. Arithmetic (including addition and subtraction) with floating point numbers, especially for planning problems, leads to incorrect decisions:

Additionally, floating point number addition is not associative:

System.out.println( ((0.01 + 0.02) + 0.03) == (0.01 + (0.02 + 0.03)) ); // returns false

This leads to score corruption.

Decimal numbers (BigDecimal) have none of these problems.

Note

BigDecimal arithmetic is considerably slower than int, long or double arithmetic. In experiments we've seen the average calculation count get divided by 5.

Therefore, in some cases, it can be worthwhile to multiply all numbers for a single score weight by a plural of ten (for example 1000), so the score weight fits in an int or long.

5.2. Choose a Score definition

Each Score implementation also has a ScoreDefinition implementation. For example: SimpleScore is definied by SimpleScoreDefinition.

5.2.1. SimpleScore

A SimpleScore has a single int value, for example -123. It has a single score level.


  <scoreDirectorFactory>

    <scoreDefinitionType>SIMPLE</scoreDefinitionType>

    ...

  </scoreDirectorFactory>

A HardSoftScore has a hard int value and a soft int value, for example -123hard/-456soft. It has 2 score levels (hard and soft).

SIMPLE_LONG: Uses SimpleLongScore which has a long value instead of an int value.
SIMPLE_DOUBLE: Uses SimpleDoubleScore which has a double value instead of an int value. Not recommended to use.
SIMPLE_BIG_DECIMAL: Uses SimpleBigDecimalScore which has a BigDecimal value instead of an int value.

5.2.2. HardSoftScore (recommended)


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    ...

  </scoreDirectorFactory>

A HardMediumSoftScore which has a hard int value, a medium int value and a soft int value, for example -123hard/-456medium/-789soft. It has 3 score levels (hard, medium and soft).

HARD_SOFT_LONG: Uses HardSoftLongScore which has long values instead of int values.
HARD_SOFT_DOUBLE: Uses HardSoftDoubleScore which has double values instead of int values. Not recommended to use.
HARD_SOFT_BIG_DECIMAL: Uses HardSoftBigDecimalScore which has BigDecimal values instead of int values.

5.2.3. HardMediumSoftScore


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_MEDIUM_SOFT</scoreDefinitionType>

    ...

  </scoreDirectorFactory>

A BendableScore has a configurable number of score levels. It has an array of hard int values and an array of soft int value, for example with 2 hard levels and 3 soft levels, the score can be -123/-456/-789/-012/-345.

HARD_MEDIUM_SOFT_LONG: Uses HardMediumSoftLongScore which has long values instead of int values.

5.2.4. BendableScore


  <scoreDirectorFactory>

    <scoreDefinitionType>BENDABLE</scoreDefinitionType>

    <bendableHardLevelsSize>2</bendableHardLevelsSize>

    <bendableSoftLevelsSize>3</bendableSoftLevelsSize>

    ...

  </scoreDirectorFactory>

The number of hard and soft score levels needs to be set at configuration time: it's not flexible to change during solving.

The ScoreDefinition interface defines the score representation.

BENDABLE_BIG_DECIMAL: Uses BendableBigDecimalScore which has BigDecimal values instead of int values.

5.2.5. Implementing a custom Score

To implement a custom Score, you'll also need to implement a custom ScoreDefinition. Extend AbstractScoreDefinition (preferable by copy pasting HardSoftScoreDefinition) and start from there.

Then hook your custom ScoreDefinition in your SolverConfig.xml:


  <scoreDirectorFactory>

    <scoreDefinitionClass>...MyScoreDefinition</scoreDefinitionClass>

    ...

  </scoreDirectorFactory>

5.3. Calculate the `Score`

5.3.1. Score calculation types

There are several ways to calculate the Score of a Solution:

Easy Java score calculation: implement a single Java method
Incremental Java score calculation: implement multiple Java methods
Drools score calculation (recommended): implement score rules

Every score calculation type can use any Score definition. For example, easy Java score calculation can output a HardSoftScore.

All score calculation types are Object Orientated and can reuse existing Java code.

Important

The score calculation should be read-only: it should not change the planning entities or the problem facts in any way. For example, it must not call a setter method on a planning entity in a Drools score rule's RHS. This does not apply to logically inserted objects, which can be changed by the score rules who logically inserted them in the first place.

OptaPlanner will not recalculate the score of a Solution if it can predict it (unless an environmentMode assertion is enabled). For example, after a winning step is done, there is no need to calculate the score because that move was done and undone earlier. As a result, there's no guarantee that such changes applied during score calculation are actually done.

5.3.2. Easy Java score calculation

An easy way to implement your score calculation in Java.

Advantages:
- Plain old Java: no learning curve
- Opportunity to delegate score calculation to an existing code base or legacy system
Disadvantages:
- Slower and less scalable
  - Because there is no incremental score calculation

Just implement one method of the interface EasyScoreCalculator:

public interface EasyScoreCalculator<Sol extends Solution> {


    Score calculateScore(Sol solution);

   

}

For example in n queens:

public class NQueensEasyScoreCalculator implements EasyScoreCalculator<NQueens> {


    public SimpleScore calculateScore(NQueens nQueens) {

        int n = nQueens.getN();

        List<Queen> queenList = nQueens.getQueenList();

        

        int score = 0;

        for (int i = 0; i < n; i++) {

            for (int j = i + 1; j < n; j++) {

                Queen leftQueen = queenList.get(i);

                Queen rightQueen = queenList.get(j);

                if (leftQueen.getRow() != null && rightQueen.getRow() != null) {

                    if (leftQueen.getRowIndex() == rightQueen.getRowIndex()) {

                        score--;

                    }

                    if (leftQueen.getAscendingDiagonalIndex() == rightQueen.getAscendingDiagonalIndex()) {

                        score--;

                    }

                    if (leftQueen.getDescendingDiagonalIndex() == rightQueen.getDescendingDiagonalIndex()) {

                        score--;

                    }

                }

            }

        }

        return SimpleScore.valueOf(score);

    }


}

Configure it in your solver configuration:


  <scoreDirectorFactory>

    <scoreDefinitionType>...</scoreDefinitionType>

    <easyScoreCalculatorClass>org.optaplanner.examples.nqueens.solver.score.NQueensEasyScoreCalculator</easyScoreCalculatorClass>

  </scoreDirectorFactory>

Alternatively, build a EasyScoreCalculator instance at runtime and set it with the programmatic API:

    solverFactory.getSolverConfig().getScoreDirectorFactoryConfig.setEasyScoreCalculator(easyScoreCalculator);

5.3.3. Incremental Java score calculation

A way to implement your score calculation incrementally in Java.

Advantages:
- Very fast and scalable
  - Currently the fastest if implemented correctly
Disadvantages:
- Hard to write
  - A scalable implementation heavily uses maps, indexes, ... (things the Drools rule engine can do for you)
  - You have to learn, design, write and improve all these performance optimizations yourself
- Hard to read
  - Regular score constraint changes can lead to a high maintenance cost

Implement all the methods of the interface IncrementalScoreCalculator and extend the class AbstractIncrementalScoreCalculator:

public interface IncrementalScoreCalculator<Sol extends Solution> {


    void resetWorkingSolution(Sol workingSolution);


    void beforeEntityAdded(Object entity);


    void afterEntityAdded(Object entity);


    void beforeVariableChanged(Object entity, String variableName);


    void afterVariableChanged(Object entity, String variableName);


    void beforeEntityRemoved(Object entity);


    void afterEntityRemoved(Object entity);


    Score calculateScore();

    

}

For example in n queens:

public class NQueensAdvancedIncrementalScoreCalculator extends AbstractIncrementalScoreCalculator<NQueens> {


    private Map<Integer, List<Queen>> rowIndexMap;

    private Map<Integer, List<Queen>> ascendingDiagonalIndexMap;

    private Map<Integer, List<Queen>> descendingDiagonalIndexMap;


    private int score;


    public void resetWorkingSolution(NQueens nQueens) {

        int n = nQueens.getN();

        rowIndexMap = new HashMap<Integer, List<Queen>>(n);

        ascendingDiagonalIndexMap = new HashMap<Integer, List<Queen>>(n * 2);

        descendingDiagonalIndexMap = new HashMap<Integer, List<Queen>>(n * 2);

        for (int i = 0; i < n; i++) {

            rowIndexMap.put(i, new ArrayList<Queen>(n));

            ascendingDiagonalIndexMap.put(i, new ArrayList<Queen>(n));

            descendingDiagonalIndexMap.put(i, new ArrayList<Queen>(n));

            if (i != 0) {

                ascendingDiagonalIndexMap.put(n - 1 + i, new ArrayList<Queen>(n));

                descendingDiagonalIndexMap.put((-i), new ArrayList<Queen>(n));

            }

        }

        score = 0;

        for (Queen queen : nQueens.getQueenList()) {

            insert(queen);

        }

    }


    public void beforeEntityAdded(Object entity) {

        // Do nothing

    }


    public void afterEntityAdded(Object entity) {

        insert((Queen) entity);

    }


    public void beforeVariableChanged(Object entity, String variableName) {

        retract((Queen) entity);

    }


    public void afterVariableChanged(Object entity, String variableName) {

        insert((Queen) entity);

    }


    public void beforeEntityRemoved(Object entity) {

        retract((Queen) entity);

    }


    public void afterEntityRemoved(Object entity) {

        // Do nothing

    }


    private void insert(Queen queen) {

        Row row = queen.getRow();

        if (row != null) {

            int rowIndex = queen.getRowIndex();

            List<Queen> rowIndexList = rowIndexMap.get(rowIndex);

            score -= rowIndexList.size();

            rowIndexList.add(queen);

            List<Queen> ascendingDiagonalIndexList = ascendingDiagonalIndexMap.get(queen.getAscendingDiagonalIndex());

            score -= ascendingDiagonalIndexList.size();

            ascendingDiagonalIndexList.add(queen);

            List<Queen> descendingDiagonalIndexList = descendingDiagonalIndexMap.get(queen.getDescendingDiagonalIndex());

            score -= descendingDiagonalIndexList.size();

            descendingDiagonalIndexList.add(queen);

        }

    }


    private void retract(Queen queen) {

        Row row = queen.getRow();

        if (row != null) {

            List<Queen> rowIndexList = rowIndexMap.get(queen.getRowIndex());

            rowIndexList.remove(queen);

            score += rowIndexList.size();

            List<Queen> ascendingDiagonalIndexList = ascendingDiagonalIndexMap.get(queen.getAscendingDiagonalIndex());

            ascendingDiagonalIndexList.remove(queen);

            score += ascendingDiagonalIndexList.size();

            List<Queen> descendingDiagonalIndexList = descendingDiagonalIndexMap.get(queen.getDescendingDiagonalIndex());

            descendingDiagonalIndexList.remove(queen);

            score += descendingDiagonalIndexList.size();

        }

    }


    public SimpleScore calculateScore() {

        return SimpleScore.valueOf(score);

    }


}

Configure it in your solver configuration:


  <scoreDirectorFactory>

    <scoreDefinitionType>...</scoreDefinitionType>

    <incrementalScoreCalculatorClass>org.optaplanner.examples.nqueens.solver.score.NQueensAdvancedIncrementalScoreCalculator</incrementalScoreCalculatorClass>

  </scoreDirectorFactory>

Optionally, to explain a score with ScoreDirector.getConstraintMatchTotals() or to get better output when the IncrementalScoreCalculator is corrupted in environmentMode FAST_ASSERT or FULL_ASSERT, implement the interface ConstraintMatchAwareIncrementalScoreCalculator too:

public interface ConstraintMatchAwareIncrementalScoreCalculator<Sol extends Solution> {


    void resetWorkingSolution(Sol workingSolution, boolean constraintMatchEnabled);


    Collection<ConstraintMatchTotal> getConstraintMatchTotals();

    

}

5.3.4. Drools score calculation

5.3.4.1. Overview

Implement your score calculation using the Drools rule engine. Every score constraint is written as one or more score rules.

Advantages:
Disadvantages:
- DRL learning curve
- Usage of DRL
  - Polyglot fear can prohibit the use of a new language such as DRL in some organizations

5.3.4.2. Drools score rules configuration

There are several ways to define where your score rules live.

5.3.4.2.1. A scoreDrl resource on the classpath

This is the easy way: the score rule live in a DRL file which is provided as a classpath resource. Just add the score rules DRL file in the solver configuration as a <scoreDrl> element:


  <scoreDirectorFactory>

    <scoreDefinitionType>...</scoreDefinitionType>

    <scoreDrl>org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl</scoreDrl>

  </scoreDirectorFactory>

In a typical project (following the Maven directory structure), that DRL file would be located at $PROJECT_DIR/src/main/resources/org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl (even for a war project).

Note

The <scoreDrl> element expects a classpath resource, as defined by ClassLoader.getResource(String), it does not accept a File, nor an URL, nor a webapp resource. See below to use a File instead.

Add multiple <scoreDrl> elements if the score rules are split across multiple DRL files.

Optionally, you can also set drools configuration properties but beware of backwards compatibility issues:


  <scoreDirectorFactory>

    ...

    <scoreDrl>org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl</scoreDrl>

    <kieBaseConfigurationProperties>

      <drools.equalityBehavior>...</drools.equalityBehavior>

    </kieBaseConfigurationProperties>

  </scoreDirectorFactory>

5.3.4.2.2. A scoreDrlFile

To use File on the local file system, instead of a classpath resource, add the score rules DRL file in the solver configuration as a <scoreDrlFile> element:


  <scoreDirectorFactory>

    <scoreDefinitionType>...</scoreDefinitionType>

    <scoreDrlFile>/home/ge0ffrey/tmp/nQueensScoreRules.drl</scoreDrlFile>

  </scoreDirectorFactory>

Warning

For portability reasons, a classpath resource is recommended over a File. An application build on one computer, but used on another computer, might not find the file on the same location. Worse, if they use a different Operating System, it's hard to choose a portable file path.

Add multiple <scoreDrlFile> elements if the score rules are split across multiple DRL files.

5.3.4.2.3. A KieBase (possibly defined by Drools Workbench)

If you prefer to build the KieBase yourself or if you're combining OptaPlanner with KIE Workbench (formerly known as Guvnor), you can set the KieBase on the SolverFactory before building the Solver:

    solverFactory.getSolverConfig().getScoreDirectorFactoryConfig.setKieBase(kieBase);

Note

To be able to define your score rules in Drools Workbench, you'll want to:

Upload the optaplanner-core jar as a POJO model.
Add a global variable called scoreHolder (see below).

5.3.4.3. Implementing a score rule

Here's an example of a score constraint implemented as a score rule in a DRL file:

rule "multipleQueensHorizontal"
    when
        Queen($id : id, row != null, $i : rowIndex)
        Queen(id > $id, rowIndex == $i)
    then
        scoreHolder.addConstraintMatch(kcontext, -1);
end

This score rule will fire once for every 2 queens with the same rowIndex. The (id > $id) condition is needed to assure that for 2 queens A and B, it can only fire for (A, B) and not for (B, A), (A, A) or (B, B). Let's take a closer look at this score rule on this solution of 4 queens:

In this solution the multipleQueensHorizontal score rule will fire for 6 queen couples: (A, B), (A, C), (A, D), (B, C), (B, D) and (C, D). Because none of the queens are on the same vertical or diagonal line, this solution will have a score of -6. An optimal solution of 4 queens has a score of 0.

Note

Notice that every score rule will relate to at least 1 planning entity class (directly or indirectly though a logically inserted fact).

This is normal: it would be a waste of time to write a score rule that only relates to problem facts, as the consequence will never change during planning, no matter what the possible solution.

Note

The variable kcontext is a magic variable in Drools Expert. The scoreHolder's method uses it to do incremental score calculation correctly and to create a ConstraintMatch instance.

5.3.4.4. Weighing score rules

A ScoreHolder instance is asserted into the KieSession as a global called scoreHolder. Your score rules need to (directly or indirectly) update that instance.

global SimpleScoreHolder scoreHolder;

rule "multipleQueensHorizontal"
    when
        Queen($id : id, row != null, $i : rowIndex)
        Queen(id > $id, rowIndex == $i)
    then
        scoreHolder.addConstraintMatch(kcontext, -1);
end

// multipleQueensVertical is obsolete because it is always 0

rule "multipleQueensAscendingDiagonal"
    when
        Queen($id : id, row != null, $i : ascendingDiagonalIndex)
        Queen(id > $id, ascendingDiagonalIndex == $i)
    then
        scoreHolder.addConstraintMatch(kcontext, -1);
end

rule "multipleQueensDescendingDiagonal"
    when
        Queen($id : id, row != null, $i : descendingDiagonalIndex)
        Queen(id > $id, descendingDiagonalIndex == $i)
    then
        scoreHolder.addConstraintMatch(kcontext, -1);
end

Most use cases will also weigh their constraint types or even their matches differently, by using a specific weight for each constraint match.

Here's an example from CurriculumCourse, where assigning a Lecture to a Room which is missing 2 seats is weighted equally bad as having 1 isolated Lecture in a Curriculum:

global HardSoftScoreHolder scoreHolder;

// RoomCapacity: For each lecture, the number of students that attend the course must be less or equal
// than the number of seats of all the rooms that host its lectures.
// Each student above the capacity counts as 1 point of penalty.
rule "roomCapacity"
    when
        $room : Room($capacity : capacity)
        $lecture : Lecture(room == $room, studentSize > $capacity, $studentSize : studentSize)
    then
        scoreHolder.addSoftConstraintMatch(kcontext, ($capacity - $studentSize));
end

// CurriculumCompactness: Lectures belonging to a curriculum should be adjacent
// to each other (i.e., in consecutive periods).
// For a given curriculum we account for a violation every time there is one lecture not adjacent
// to any other lecture within the same day.
// Each isolated lecture in a curriculum counts as 2 points of penalty.
rule "curriculumCompactness"
    when
        ...
    then
        scoreHolder.addSoftConstraintMatch(kcontext, -2);
end

5.3.5. InitializingScoreTrend

The InitializingScoreTrend specifies how the Score will change as more and more variables are initialized (while the already variables don't change). Some optimization algorithms (such Construction Heuristics and Exhaustive Search) run faster if they have such information.

For for the Score (or each score level separately), specify a trend:

ANY (default): Initializing an extra variable can change the score positively or negatively. Gives no performance gain.
ONLY_UP (rare): Initializing an extra variable can only change the score positively. Implies that:
- There are only positive constraints
- Initializing the next variable can unmatch a positive constraint matched by a previous initialized variable.
ONLY_DOWN: Initializing an extra variable can only change the score negatively. Implies that:
- There are only negative constraints
- Initializing the next variable can unmatch a negative constraint matched by a previous initialized variable.

Most use cases only have negative constraints. Many of those have an InitializingScoreTrend that only goes down:


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    <scoreDrl>.../cloudBalancingScoreRules.drl</scoreDrl>

    <initializingScoreTrend>ONLY_DOWN</initializingScoreTrend>

  </scoreDirectorFactory>

Alternatively, you can also specify the trend for each score level separately:


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    <scoreDrl>.../cloudBalancingScoreRules.drl</scoreDrl>

    <initializingScoreTrend>ONLY_DOWN/ONLY_DOWN</initializingScoreTrend>

  </scoreDirectorFactory>

5.3.6. Invalid score detection

Put the environmentMode in FULL_ASSERT (or FAST_ASSERT) to detect corruption in the incremental score calculation. For more information, see the section about environmentMode. However, that will not verify that your score calculator implements your score constraints as your business actually desires.

A piece of incremental score calculator code can be difficult to write and to review. Assert its correctness by using a different implementation (for example a EasyScoreCalculator) to do the assertions triggered by the environmentMode. Just configure the different implementation as a assertionScoreDirectorFactory:


  <environmentMode>FAST_ASSERT</environmentMode>

  ...

  <scoreDirectorFactory>

    <scoreDefinitionType>...</scoreDefinitionType>

    <scoreDrl>org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl</scoreDrl>

    <assertionScoreDirectorFactory>

      <easyScoreCalculatorClass>org.optaplanner.examples.nqueens.solver.score.NQueensEasyScoreCalculator</easyScoreCalculatorClass>

    </assertionScoreDirectorFactory>

  </scoreDirectorFactory>

This way, the scoreDrl will be validated by the EasyScoreCalculator.

5.4. Score calculation performance tricks

5.4.1. Overview

The Solver will normally spend most of its execution time running the score calculation (which is called in its deepest loops). Faster score calculation will return the same solution in less time with the same algorithm, which normally means a better solution in equal time.

5.4.2. Average calculation count per second

After solving a problem, the Solver will log the average calculation count per second. This is a good measurement of Score calculation performance, despite that it is affected by non score calculation execution time. It depends on the problem scale of the problem dataset. Normally, even for high scale problems, it is higher than 1000, except when you're using EasyScoreCalculator.

Important

When improving your score calculation, focus on maximizing the average calculation count per second, instead of maximizing the best score. A big improvement in score calculation can sometimes yield little or no best score improvement, for example when the algorithm is stuck in a local or global optima. If you're watching the calculation count instead, score calculation improvements are far more visible.

Furthermore, watching the calculation count, allows you to remove or add score constraints, and still compare it with the original calculation count. Comparing the best score with the original would be wrong, because it's comparing apples and oranges.

5.4.3. Incremental score calculation (with delta's)

When a Solution changes, incremental score calculation (AKA delta based score calculation), will calculate the delta with the previous state to find the new Score, instead of recalculating the entire score on every solution evaluation.

For example, if a single queen A moves from row 1 to 2, it won't bother to check if queen B and C can attack each other, since neither of them changed.

Figure 5.1. Incremental score calculation for the 4 queens puzzle

This is a huge performance and scalability gain. Drools score calculation gives you this huge scalability gain without forcing you to write a complicated incremental score calculation algorithm. Just let the Drools rule engine do the hard work.

Notice that the speedup is relative to the size of your planning problem (your n), making incremental score calculation far more scalable.

5.4.4. Avoid calling remote services during score calculation

Do not call remote services in your score calculation (except if you're bridging EasyScoreCalculator to a legacy system). The network latency will kill your score calculation performance. Cache the results of those remote services if possible.

If some parts of a constraint can be calculated once, when the Solver starts, and never change during solving, then turn them into cached problem facts.

5.4.5. Pointless constraints

If you know a certain constraint can never be broken (or it is always broken), don't bother writing a score constraint for it. For example in n queens, the score calculation doesn't check if multiple queens occupy the same column, because a Queen's column never changes and every Solution starts with each Queen on a different column.

Note

Don't go overboard with this. If some datasets don't use a specific constraint but others do, just return out of the constraint as soon as you can. There is no need to dynamically change your score calculation based on the dataset.

5.4.6. Build-in hard constraint

Instead of implementing a hard constraint, you can sometimes make it build-in too. For example: If Lecture A should never be assigned to Room X, but it uses ValueRangeProvider on Solution, the Solver will often try to assign it to Room X too (only to find out that it breaks a hard constraint). Use filtered selection to define that Course A should only be assigned a Room other than X.

This tends to give a good performance gain, not just because the score calculation is faster, but mainly because most optimization algorithms will spend less time evaluating unfeasible solutions.

Note

Don't go overboard with this. Many optimization algorithms rely on the freedom to break hard constraints when changing planning entities, to get out of local optima. There is a real risk of trading short term benefits for long term harm.

5.4.7. Other performance tricks

Verify that your score calculation happens in the correct Number type. If you're making the sum of int values, don't let Drools sum it in a double which takes longer.
For optimal performance, always use server mode (java -server). We have seen performance increases of 50% by turning on server mode.
For optimal performance, use the latest Java version. For example, in the past we have seen performance increases of 30% by switching from java 1.5 to 1.6.
Always remember that premature optimization is the root of all evil. Make sure your design is flexible enough to allow configuration based tweaking.

5.4.8. Score trap

Make sure that none of your score constraints cause a score trap. A trapped score constraint uses the same weight for different constraint matches, when it could just as easily use a different weight. It effectively lumps its constraint matches together, which creates a flatlined score function for that constraint. This can cause a solution state in which several moves need to be done to resolve or lower the weight of that single constraint. Some examples of score traps:

If you need 2 doctors at each table, but you're only moving 1 doctor at a time. So the solver has no incentive to move a doctor to a table with no doctors. Punish a table with no doctors more then a table with only 1 doctor in that score constraint in the score function.
2 exams needs to be conducted at the same time, but you're only move 1 exam at a time. So the solver has a disincentive move one of those exams to another timeslot without moving the other in the same move. Add a course-grained move that moves both exams at the same time.

For example, consider this score trap. If the blue item moves from an overloaded computer to an empty computer, the hard score should improve. The trapped score implementation fails to do that:

The Solver should eventually get out of this trap, but it will take a lot of effort (especially if there are even more processes on the overloaded computer). Before they do that, they might actually start moving more processes into that overloaded computer, as there is no penalty for doing so.

Note

Avoiding score traps does not mean that your score function should be smart enough to avoid local optima. Leave it to the optimization algorithms to deal with the local optima.

Avoiding score traps means to avoid - for each score constraint individually - a flatlined score function.

Important

Always specify the degree of infeasibility. The business will often say: "if the solution is infeasible, it doesn't matter how infeasible it." While that's true for the business, it's not true for score calculation: it benefits from knowing how infeasible it is. In practice, soft constraints usually do this naturally and it's just a matter of doing it for the hard constraints too.

There are several ways to deal with a score trap:

Improve the score constraint to make a distinction in the score weight. For example: penalize -1hard for every missing CPU, instead of just -1hard if any CPU is missing.
If changing the score constraint is not allowed from the business perspective, add a lower score level with a score constraint that makes such a distinction. For example: penalize -1subsoft for every missing CPU, on top of -1hard if any CPU is missing. The business ignores the subsoft score level.
Add course-grained moves and union select them with the existing fine-grained moves. A course-grained move effectively does multiple moves to directly get out of a score trap with a single move. For example: move multiple items from the same container to another container.

5.4.9. stepLimit benchmark

Not all score constraints have the same performance cost. Sometimes 1 score constraint can kill the score calculation performance outright. Use the Benchmarker to do a 1 minute run and check what happens to the average calculation count per second if you comment out all but 1 of the score constraints.

5.4.10. Fairness score constraints

Some use cases have a business requirement to provide a fair schedule (usually as a soft score constraint), for example:

Fairly distribute the workload amongst the employees, to avoid envy.
Evenly distribute the workload amongst assets, to improve reliability.

Implementing such a constraint can seem difficult (especially because there are different ways to formalize fairness), but usually the squared workload implementation behaves most desirable: for each employee/asset, count the workload w and subtract the square w² from the score.

As shown above, the squared workload implementation guarantees that if you select 2 employees from a given solution and make their distribution between those 2 employees more fair that the resulting new solution will have a better overall score.

5.5. Explaining the score: using score calculation outside the `Solver`

Other parts of your application, for example your webUI, might need to calculate the score too. Do that by reusing the ScoreDirectorFactory of the Solver to build a separate ScoreDirector for that webUI:

ScoreDirectorFactory scoreDirectorFactory = solver.getScoreDirectorFactory();

ScoreDirector guiScoreDirector = scoreDirectorFactory.buildScoreDirector();

Then use it when you need to calculate the Score of a Solution:

guiScoreDirector.setWorkingSolution(solution);

Score score = guiScoreDirector.calculateScore();

To explain in the GUI what entities are causing which part of the Score, get the ConstraintMatch objects from the ScoreDirector:

for (ConstraintMatchTotal constraintMatchTotal : guiScoreDirector.getConstraintMatchTotals()) {

    String constraintName = constraintMatchTotal.getConstraintName();

    Number weightTotal = constraintMatchTotal.getWeightTotalAsNumber();

    for (ConstraintMatch constraintMatch : constraintMatchTotal.getConstraintMatchSet()) {

        List<Object> justificationList = constraintMatch.getJustificationList();

        Number weight = constraintMatch.getWeightAsNumber();

        ...

    }

}

Note

Drools score calculation supports constraint matches automatically, but incremental Java score calculation requires requires implementing an extra interface (see that section).

Chapter 6. Optimization algorithms

6.1. Search space size in the real world

6.2. Does Planner find the optimal solution?

6.3. Architecture overview

6.4. Optimization algorithms overview

6.5. Which optimization algorithms should I use?

6.6. Solver phase

6.7. Scope overview

6.8. Termination

6.8.1. TimeMillisSpentTermination
6.8.2. UnimprovedTimeMillisSpentTermination
6.8.3. BestScoreTermination
6.8.4. BestScoreFeasibleTermination
6.8.5. StepCountTermination
6.8.6. UnimprovedStepCountTermination
6.8.7. Combining multiple Terminations
6.8.8. Asynchronous termination from another thread

6.9. SolverEventListener

6.10. Custom solver phase

6.1. Search space size in the real world

The number of possible solutions for a planning problem can be mind blowing. For example:

4 queens has 256 possible solutions (4^4) and 2 optimal solutions.
5 queens has 3125 possible solutions (5^5) and 1 optimal solution.
8 queens has 16777216 possible solutions (8^8) and 92 optimal solutions.
64 queens has more than 10^115 possible solutions (64^64).
Most real-life planning problems have an incredible number of possible solutions and only 1 or a few optimal solutions.

For comparison: the minimal number of atoms in the known universe (10^80). As a planning problem gets bigger, the search space tends to blow up really fast. Adding only 1 extra planning entity or planning value can heavily multiply the running time of some algorithms.

Calculating the number of possible solutions depends on the design of the domain model:

Note

This search space size calculation includes infeasible solutions (if they can be represented by the model), because:

The optimal solution might be infeasible.
There are many types of hard constraints which cannot be incorporated in the formula practically. For example in Cloud Balancing, try incorporating the CPU capacity constraint in the formula.

Even in cases were adding some of the hard constraints in the formula is practical, for example Course Scheduling, the resulting search space is still huge.

An algorithm that checks every possible solution (even with pruning such as in Branch And Bound) can easily run for billions of years on a single real-life planning problem. What we really want is to find the best solution in the limited time at our disposal. Planning competitions (such as the International Timetabling Competition) show that Local Search variations (Tabu Search, Simulated Annealing, Late Acceptance, ...) usually perform best for real-world problems given real-world time limitations.

6.2. Does Planner find the optimal solution?

The business wants the optimal solution, but they also have other requirements:

Scale out: Large production datasets must not crash and have good results too.
Optimize the right problem: The constraints must match the actual business needs.
Available time: The solution must be found in time, before it becomes useless to execute.
Reliability: Every dataset must have at least a decent result (better than a human planner).

Given these requirements, and despite the promises of some salesmen, it's usually impossible for anyone or anything to find the optimal solution. Therefore, OptaPlanner focuses on finding the best solution in available time. In realistic, independent competitions, OptaPlanner often comes out as the best reusable software.

The nature of NP-complete problems make scaling a prime concern. The result quality of a small dataset guarantees nothing about the result quality of a large dataset. Scaling issues cannot be mitigated by hardware purchases later on. Start testing with a production sized dataset as soon as possible. Don't asses quality on small datasets (unless production encounters only such datasets). Instead, solve a production sized dataset and compare the results of longer executions, different algorithms and - if available - the human planner.

6.3. Architecture overview

OptaPlanner is the first framework to combine optimization algorithms (metaheuristics, ...) with score calculation by a rule engine such as Drools Expert. This combination turns out to be a very efficient, because:

A rule engine such as Drools Expert is great for calculating the score of a solution of a planning problem. It makes it easy and scalable to add additional soft or hard constraints such as "a teacher shouldn't teach more then 7 hours a day". It does delta based score calculation without any extra code. However it tends to be not suitable to actually find new solutions.
An optimization algorithm is great at finding new improving solutions for a planning problem, without necessarily brute-forcing every possibility. However it needs to know the score of a solution and offers no support in calculating that score efficiently.

6.4. Optimization algorithms overview

Table 6.1. Optimization algorithms overview

Algorithm	Scalable?	Optimal?	Easy to use?	Tweakable?	Requires CH?
Exhaustive Search
Brute Force	0/5	5/5	5/5	0/5	No
Branch And Bound	0/5	5/5	4/5	2/5	No
Construction heuristics (CH)
First Fit	5/5	1/5	5/5	1/5	No
First Fit Decreasing	5/5	2/5	4/5	2/5	No
Weakest Fit	5/5	2/5	4/5	2/5	No
Weakest Fit Decreasing	5/5	2/5	4/5	2/5	No
Strongest Fit	5/5	2/5	4/5	2/5	No
Strongest Fit Decreasing	5/5	2/5	4/5	2/5	No
Cheapest Insertion	3/5	2/5	5/5	2/5	No
Regret Insertion	3/5	2/5	5/5	2/5	No
Metaheuristics (MH)
Local Search
Hill Climbing	5/5	2/5	4/5	3/5	Yes
Tabu Search	5/5	4/5	3/5	5/5	Yes
Simulated Annealing	5/5	4/5	2/5	5/5	Yes
Late Acceptance	5/5	4/5	3/5	5/5	Yes
Step Counting Hill Climbing	5/5	4/5	3/5	5/5	Yes
Evolutionary Algorithms
Evolutionary Strategies	4/5	3/5	2/5	5/5	Yes
Genetic Algorithms	4/5	3/5	2/5	5/5	Yes

If you want to learn more about metaheuristics, read the free books Essentials of Metaheuristics or Clever Algorithms.

6.5. Which optimization algorithms should I use?

The best optimization algorithms configuration for your use case depends heavily on your use case. Nevertheless, this vanilla recipe will get you into the game with a pretty good configuration, probably much better than what you're used to.

Start with a quick configuration that involves little or no configuration and optimization code:

First Fit

Next, implement planning entity difficulty comparison and turn it into:

First Fit Decreasing

Next, add Late Acceptance behind it:

First Fit Decreasing
Late Acceptance. A late acceptance size of 400 usually works well.

At this point the free lunch is over. The return on invested time lowers. The result is probably already more than good enough.

But you can do even better, at a lower return on invested time. Use the Benchmarker and try a couple of different Tabu Search, Simulated Annealing and Late Acceptance configurations, for example:

First Fit Decreasing
Tabu Search. An entity tabu size of 7 usually works well.

Use the Benchmarker to improve the values for those size parameters.

If it's worth your time, continue experimenting further. For example, you can even combine multiple algorithms together:

First Fit Decreasing
Late Acceptance (relatively long time)
Tabu Search (relatively short time)

6.6. Solver phase

A Solver can use multiple optimization algorithms in sequence. Each optimization algorithm is represented by a solver Phase. There is never more than 1 Phase solving at the same time.

Note

Some Phase implementations can combine techniques from multiple optimization algorithms, but they are still just 1 Phase. For example: a local search Phase can do simulated annealing with entity tabu.

Here's a configuration that runs 3 phases in sequence:


<solver>

  ...

  <constructionHeuristic>

    ... <!-- First phase: First Fit Decreasing -->

  </constructionHeuristic>

  <localSearch>

    ... <!-- Second phase: Late acceptance -->

  </localSearch>

  <localSearch>

    ... <!-- Third phase: Tabu Search -->

  </localSearch>

</solver>

The solver phases are run in the order defined by solver configuration. When the first Phase terminates, the second Phase starts, and so on. When the last Phase terminates, the Solver terminates. Usually, a Solver will first run a construction heuristic and then run 1 or multiple metaheuristics:

Some phases (especially construction heuristics) will terminate automatically. Other phases (especially metaheuristics) will only terminate if the Phase is configured to terminate:


<solver>

  ...

  <termination><!-- Solver termination -->

    <secondsSpentLimit>90</secondsSpentLimit>

  </termination>

  <localSearch>

    <termination><!-- Phase termination -->

      <secondsSpentLimit>60</secondsSpentLimit><!-- Give the next phase a chance to run too, before the Solver terminates -->

    </termination>

    ...

  </localSearch>

  <localSearch>

    ...

  </localSearch>

</solver>

If the Solver terminates (before the last Phase terminates itself), the current phase is terminated and all subsequent phases won't run.

6.7. Scope overview

A solver will iteratively run phases. Each phase will usually iteratively run steps. Each step, in turn, usually iteratively runs moves. These form 4 nested scopes: solver, phase, step and move.

Configure logging to display the log messages of each scope.

6.8. Termination

Not all phases terminate automatically and sometimes you don't want to wait that long anyway. A Solver can be terminated synchronously by up-front configuration or asynchronously from another thread.

Especially metaheuristic phases will need to be told when to stop solving. This can be because of a number of reasons: the time is up, the perfect score has been reached, ... The only thing you can't depend on, is on finding the optimal solution (unless you know the optimal score), because a metaheuristic algorithm generally doesn't know it when it finds the optimal solution. For real-life problems this doesn't turn out to be much of a problem, because finding the optimal solution could take billions of years, so you 'll want to terminate sooner anyway. The only thing that matters is finding the best solution in the available time.

For synchronous termination, configure a Termination on a Solver or a Phase when it needs to stop. You can implement your own Termination, but the build-in implementations should suffice for most needs. Every Termination can calculate a time gradient (needed for some optimization algorithms), which is a ratio between the time already spent solving and the estimated entire solving time of the Solver or Phase.

6.8.1. TimeMillisSpentTermination

Terminates when an amount of time has been used:


  <termination>

    <millisecondsSpentLimit>500</millisecondsSpentLimit>

  </termination>


  <termination>

    <secondsSpentLimit>10</secondsSpentLimit>

  </termination>


  <termination>

    <minutesSpentLimit>5</minutesSpentLimit>

  </termination>


  <termination>

    <hoursSpentLimit>1</hoursSpentLimit>

  </termination>

Note

This Termination will most likely sacrifice perfect reproducibility (even with environmentMode REPRODUCIBLE) because the available CPU time differs frequently between runs:

The available CPU time influences the number of steps that can be taken, which might be a few more or less.
The Termination might produce slightly different time gradient values, which will send time gradient based algorithms (such as Simulated Annealing) on a radically different path.

6.8.2. UnimprovedTimeMillisSpentTermination

Terminates when the best score hasn't improved in an amount of time:


  <localSearch>

    <termination>

      <unimprovedMillisecondsSpentLimit>500</unimprovedMillisecondsSpentLimit>

    </termination>

  </localSearch>


  <localSearch>

    <termination>

      <unimprovedSecondsSpentLimit>10</unimprovedSecondsSpentLimit>

    </termination>

  </localSearch>


  <localSearch>

    <termination>

      <unimprovedMinutesSpentLimit>5</unimprovedMinutesSpentLimit>

    </termination>

  </localSearch>


  <localSearch>

    <termination>

      <unimprovedHoursSpentLimit>1</unimprovedHoursSpentLimit>

    </termination>

  </localSearch>

This termination should not be applied to Construction Heuristics, because they only update the best solution at the end. Therefore it might be better to configure it on a specific Phase (such as <localSearch>), instead of on the Solver itself.

Note

This Termination will most likely sacrifice perfect reproducibility (even with environmentMode REPRODUCIBLE) because the available CPU time differs frequently between runs:

The available CPU time influences the number of steps that can be taken, which might be a few more or less.
The Termination might produce slightly different time gradient values, which will send time gradient based algorithms (such as Simulated Annealing) on a radically different path.

6.8.3. BestScoreTermination

Terminates when a certain score has been reached. You can use this Termination if you know the perfect score, for example for 4 queens (which uses a SimpleScore):


  <termination>

    <bestScoreLimit>0</bestScoreLimit>

  </termination>

For a planning problem with a HardSoftScore, it could look like this:


  <termination>

    <bestScoreLimit>0hard/-5000soft</bestScoreLimit>

  </termination>

For a planning problem with a BendableScore with 3 hard levels and 1 soft level, it could look like this:


  <termination>

    <bestScoreLimit>0/0/0/-5000</bestScoreLimit>

  </termination>

To terminate once a feasible solution has been reached, this Termination isn't practical because it requires a bestScoreLimit such as 0hard/-2147483648soft. Instead, use the next termination.

6.8.4. BestScoreFeasibleTermination

Terminates when a certain score is feasible. Requires that the Score implementation implements FeasibilityScore.


  <termination>

    <bestScoreFeasible>true</bestScoreFeasible>

  </termination>

This Termination is usually combined with other terminations.

6.8.5. StepCountTermination

Terminates when an amount of steps has been reached:


  <localSearch>

    <termination>

      <stepCountLimit>100</stepCountLimit>

    </termination>

  </localSearch>

This Termination can only be used for a Phase (such as <localSearch>), not for the Solver itself.

6.8.6. UnimprovedStepCountTermination

Terminates when the best score hasn't improved in a number of steps:


  <localSearch>

    <termination>

      <unimprovedStepCountLimit>100</unimprovedStepCountLimit>

    </termination>

  </localSearch>

If the score hasn't improved recently, it's probably not going to improve soon anyway and it's not worth the effort to continue. We have observed that once a new best solution is found (even after a long time of no improvement on the best solution), the next few steps tend to improve the best solution too.

This Termination can only be used for a Phase (such as <localSearch>), not for the Solver itself.

6.8.7. Combining multiple Terminations

Terminations can be combined, for example: terminate after 100 steps or if a score of 0 has been reached:


  <termination>

    <terminationCompositionStyle>OR</terminationCompositionStyle>

    <stepCountLimit>100</stepCountLimit>

    <bestScoreLimit>0</bestScoreLimit>

  </termination>

Alternatively you can use AND, for example: terminate after reaching a feasible score of at least -100 and no improvements in 5 steps:


  <termination>

    <terminationCompositionStyle>AND</terminationCompositionStyle>

    <unimprovedStepCountLimit>5</unimprovedStepCountLimit>

    <bestScoreLimit>-100</bestScoreLimit>

  </termination>

This example ensures it doesn't just terminate after finding a feasible solution, but also completes any obvious improvements on that solution before terminating.

6.8.8. Asynchronous termination from another thread

Sometimes you'll want to terminate a Solver early from another thread, for example because a user action or a server restart. This cannot be configured by a Termination as it's impossible to predict when and if it will occur. Therefore the Solver interface has these 2 thread-safe methods:

public interface Solver {


    // ...


    boolean terminateEarly();

    boolean isTerminateEarly();


}

If you call the terminateEarly() method from another thread, the Solver will terminate at its earliest convenience and the solve(Solution) method will return in the original Solver thread.

6.9. SolverEventListener

Each time a new best solution is found, the Solver fires a BestSolutionChangedEvent, in the solver's thread.

To listen to such events, add a SolverEventListener to the Solver:

public interface Solver {


    // ...


    void addEventListener(SolverEventListener<? extends Solution> eventListener);

    void removeEventListener(SolverEventListener<? extends Solution> eventListener);


}

The BestSolutionChangedEvent's newBestSolution might not be initialized or feasible. Use the methods on BestSolutionChangedEvent to detect such cases:

    solver.addEventListener(new SolverEventListener<CloudBalance>() {

        public void bestSolutionChanged(BestSolutionChangedEvent<CloudBalance> event) {

            // Ignore invalid solutions

            if (event.isNewBestSolutionInitialized()

                    && event.getNewBestSolution().getScore().isFeasible()) {

                ...

            }

        }

    });

Warning

The bestSolutionChanged() method is called in the solver's thread, as part of Solver.solve(). So it should return quickly to avoid slowing down the solving.

6.10. Custom solver phase

Between phases or before the first phase, you might want to execute a custom action on the Solution to get a better score. Yet you'll still want to reuse the score calculation. For example, to implement a custom construction heuristic without implementing an entire Phase.

Note

Most of the time, a custom construction heuristic is not worth the hassle. The supported constructions heuristics are configurable (use the Benchmarker to tweak them), Termination aware and support partially initialized solutions too.

Implement the CustomPhaseCommand interface:

public interface CustomPhaseCommand {


    void changeWorkingSolution(ScoreDirector scoreDirector);


}

For example:

public class ToOriginalMachineSolutionInitializer implements CustomPhaseCommand {


    public void changeWorkingSolution(ScoreDirector scoreDirector) {

        MachineReassignment machineReassignment = (MachineReassignment) scoreDirector.getWorkingSolution();

        for (MrProcessAssignment processAssignment : machineReassignment.getProcessAssignmentList()) {

            scoreDirector.beforeVariableChanged(processAssignment, "machine");

            processAssignment.setMachine(processAssignment.getOriginalMachine());

            scoreDirector.afterVariableChanged(processAssignment, "machine");

        }

    }


}

Warning

Any change on the planning entities in a CustomPhaseCommand must be notified to the ScoreDirector.

Warning

Do not change any of the planning facts in a CustomPhaseCommand. That will corrupt the Solver because any previous score or solution was for a different problem. To do that, read about repeated planning and do it with a ProblemFactChange instead.

Configure your CustomPhaseCommand like this:


<solver>

  ...

  <customPhase>

    <customPhaseCommandClass>org.optaplanner.examples.machinereassignment.solver.solution.initializer.ToOriginalMachineSolutionInitializer</customPhaseCommandClass>

  </customPhase>

  ... <!-- Other phases -->

</solver>

Configure multiple customPhaseCommandClass instances to run them in sequence.

Important

If the changes of a CustomPhaseCommand don't result in a better score, the best solution won't be changed (so effectively nothing will have changed for the next Phase or CustomPhaseCommand). To force such changes anyway, use forceUpdateBestSolution:


  <customPhase>

    <customPhaseCommandClass>...MyUninitializer</customPhaseCommandClass>

    <forceUpdateBestSolution>true</forceUpdateBestSolution>

  </customPhase>

Note

If the Solver or a Phase wants to terminate while a CustomPhaseCommand is still running, it will wait to terminate until the CustomPhaseCommand is done, however long that takes.

Chapter 7. `Move` and neighborhood selection

7.1. Move and neighborhood introduction

7.1.1. What is a Move?
7.1.2. What is a MoveSelector?
7.1.3. Subselecting of entities, values and other moves

7.2. Generic MoveSelectors

7.2.1. changeMoveSelector
7.2.2. swapMoveSelector
7.2.3. pillarChangeMoveSelector
7.2.4. pillarSwapMoveSelector
7.2.5. subChainChangeMoveSelector
7.2.6. subChainSwapMoveSelector

7.3. Combining multiple MoveSelectors

7.3.1. unionMoveSelector
7.3.2. cartesianProductMoveSelector

7.4. EntitySelector

7.5. ValueSelector

7.6. General Selector features

7.6.1. CacheType: Create moves ahead of time or Just In Time
7.6.2. SelectionOrder: original, sorted, random, shuffled or probabilistic
7.6.3. Recommended combinations of CacheType and SelectionOrder
7.6.4. Filtered selection
7.6.5. Sorted selection
7.6.6. Probabilistic selection
7.6.7. Limited selection
7.6.8. Mimic selection (record/replay)

7.7. Custom moves

7.7.1. Which move types might be missing in my implementation?
7.7.2. Custom moves introduction
7.7.3. The interface Move
7.7.4. MoveListFactory: the easy way to generate custom moves
7.7.5. MoveIteratorFactory: generate custom moves just in time

7.1. `Move` and neighborhood introduction

7.1.1. What is a `Move`?

A Move is a change (or set of changes) from a solution A to a solution B. For example, the move below changes queen C from row 0 to row 2:

The new solution is called a neighbor of the original solution, because it can be reached in a single Move. Although a single move can change multiple queens, the neighbors of a solution should always be a very small subset of all possible solutions. For example, on that original solution, these are all possible changeMove's:

If we ignore the 4 changeMove's that have not impact and are therefore not doable, we can see that number of moves is n * (n - 1) = 12. This is far less than the number of possible solutions, which is n ^ n = 256. As the problem scales out, the number of possible moves increases far less than the number of possible solutions.

Yet, in 4 changeMove's or less we can reach any solution. For example we can reach a very different solution in 3 changeMove's:

Note

There are many other types of moves besides changeMove's. Many move types are included out-of-the-box, but you can also implement custom moves.

A Move can affect multiple entities or even create/delete entities. But it must not change the problem facts.

All optimization algorithms use Move's to transition from one solution to a neighbor solution. Therefor, all the optimization algorithms are confronted with Move selection: the craft of creating and iterating moves efficiently and the art of finding the most promising subset of random moves to evaluate first.

7.1.2. What is a `MoveSelector`?

A MoveSelector's main function is to create Iterator<Move> when needed. An optimization algorithm will iterate through a subset of those moves.

Here's an example how to configure a changeMoveSelector for the optimization algorithm Local Search:


  <localSearch>

    <changeMoveSelector/>

    ...

  </localSearch>

Out of the box, this works and all properties of the changeMoveSelector are defaulted sensibly (unless that fails fast due to ambiguity). On the other hand, the configuration can be customized significantly for specific use cases. For example: you might want to configure a filter to discard pointless moves.

7.1.3. Subselecting of entities, values and other moves

To create a Move, a MoveSelector needs to select 1 or more planning entities and/or planning values to move. Just like MoveSelectors, EntitySelectors and ValueSelectors need to support a similar feature set (such as scalable just-in-time selection). Therefore, they all implement a common interface Selector and they are configured similarly.

A MoveSelector is often composed out of EntitySelectors, ValueSelectors or even other MoveSelectors, which can be configured individually if desired:


    <unionMoveSelector>

      <changeMoveSelector>

        <entitySelector>

          ...

        </entitySelector>

        <valueSelector>

          ...

        </valueSelector>

        ...

      </changeMoveSelector>

      <swapMoveSelector>

        ...

      </swapMoveSelector>

    </unionMoveSelector>

Together, this structure forms a Selector tree:

The root of this tree is a MoveSelector which is injected into the optimization algorithm implementation to be (partially) iterated in every step.

7.2. Generic MoveSelectors

7.2.1. `changeMoveSelector`

For 1 planning variable, the ChangeMove selects 1 planning entity and 1 planning value and assigns the entity's variable to that value.


    <changeMoveSelector/>

A ChangeMove is the finest grained move.


    <changeMoveSelector>

      ... <!-- Normal selector properties -->

      <entitySelector>

        <entityClass>...Lecture</entityClass>

        ...

      </entitySelector>

      <valueSelector>

        <variableName>room</variableName>

        ...

      </valueSelector>

    </changeMoveSelector>

Important

Almost every moveSelector configuration injected into a metaheuristic algorithm should include a changeMoveSelector or a custom implementation. This guarantees that every possible Solution can be reached through applying a number of moves in sequence (not taking score traps into account). Of course, normally it is unioned with other, more course grained move selectors.

7.2.2. swapMoveSelector

The SwapMove selects 2 different planning entities and swaps the planning values of all their planning variables.

Although a SwapMove on a single variable is essentially just 2 ChangeMoves, it's often the winning step where the first of the 2 ChangeMoves would not be the winning step because it leave the solution in a state with broken hard constraints. For example: swapping the room of 2 lectures doesn't bring the solution in a intermediate state where both lectures are in the same room which breaks a hard constraint.


    <swapMoveSelector/>

The secondaryEntitySelector is rarely needed: if it is not specified, entities from the same entitySelector are swapped.


    <swapMoveSelector>

      ... <!-- Normal selector properties -->

      <entitySelector>

        <entityClass>...Lecture</entityClass>

        ...

      </entitySelector>

      <secondaryEntitySelector>

        ...

      </secondaryEntitySelector>

      <variableNameInclude>room</variableNameInclude>

      <variableNameInclude>...</variableNameInclude>

    </swapMoveSelector>

If one or more variableNameInclude properties are specified, not all planning variables will be swapped, but only those specified. For example for course scheduling, specifying only variableNameInclude room will make it only swap room, not period.

7.2.3. pillarChangeMoveSelector

A pillar is a set of planning entities which have the same planning value(s) for their planning variable(s). The PillarChangeMove selects 1 entity pillar (or subset of those) and changes the value of 1 variable (which is the same for all entities) to another value.

In the example above, queen A and C have the same value (row 0) and are moved to row 2. Also the yellow and blue process have the same value (computer Y) and are moved to computer X.


    <pillarChangeMoveSelector/>

A sub pillar is a subset of entities that share the same value(s) for their variable(s). For example if queen A, B, C and D are all located on row 0, they are a pillar and [A, D] is one of the many sub pillars. If subPillarEnabled (defaults to true) is false, no sub pillars are selected. If sub pillars are enabled, the pillar itself is also included and the properties minimumSubPillarSize (defaults to 1) and maximumSubPillarSize (defaults to infinity) limit the size of the selected (sub) pillar.


    <pillarSwapMoveSelector>

      ... <!-- Normal selector properties -->

      <pillarSelector>

        <entitySelector>

          <entityClass>...Lecture</entityClass>

          ...

        </entitySelector>

        <subPillarEnabled>true</subPillarEnabled>

        <minimumSubPillarSize>1</minimumSubPillarSize>

        <maximumSubPillarSize>1000</maximumSubPillarSize>

      </pillarSelector>

      <valueSelector>

        <variableName>room</variableName>

        ...

      </valueSelector>

    </pillarSwapMoveSelector>

Note

The number of sub pillars of a pillar is exponential to the size of the pillar. For example a pillar of size 32 has (2^32 - 1) subpillars. Therefore a pillarSelector only supports JIT random selection (which is the default).

The other properties are explained in changeMoveSelector.

7.2.4. pillarSwapMoveSelector

A pillar is a set of planning entities which have the same planning value(s) for their planning variable(s). The PillarSwapMove selects 2 different entity pillars and swaps the values of all their variables for all their entities.


    <pillarSwapMoveSelector/>

The secondaryPillarSelector is rarely needed: if it is not specified, entities from the same pillarSelector are swapped.


    <pillarSwapMoveSelector>

      ... <!-- Normal selector properties -->

      <pillarSelector>

        <entitySelector>

          <entityClass>...Lecture</entityClass>

          ...

        </entitySelector>

        <subPillarEnabled>true</subPillarEnabled>

        <minimumSubPillarSize>1</minimumSubPillarSize>

        <maximumSubPillarSize>1000</maximumSubPillarSize>

      </pillarSelector>

      <secondaryPillarSelector>

        <entitySelector>

          ...

        </entitySelector>

        ...

      </secondaryPillarSelector>

      <variableNameInclude>room</variableNameInclude>

      <variableNameInclude>...</variableNameInclude>

    </pillarSwapMoveSelector>

The other properties are explained in swapMoveSelector and pillarChangeMoveSelector.

7.2.5. subChainChangeMoveSelector

A subChain is a set of planning entities with a chained planning variable which form part of a chain. The subChainChangeMove selects a subChain and moves it to another place in a different or the same anchor chain.


    <subChainChangeMoveSelector/>

The subChainSelector selects a number of entities, no less than minimumSubChainSize (defaults to 1) and no more than maximumSubChainSize (defaults to infinity).


    <subChainChangeMoveSelector>

      ... <!-- Normal selector properties -->

      <entityClass>...Customer</entityClass>

      <subChainSelector>

        <valueSelector>

          <variableName>previousStandstill</variableName>

          ...

        </valueSelector>

        <minimumSubChainSize>2</minimumSubChainSize>

        <maximumSubChainSize>40</maximumSubChainSize>

      </subChainSelector>

      <valueSelector>

        <variableName>previousStandstill</variableName>

        ...

      </valueSelector>

      <selectReversingMoveToo>true</selectReversingMoveToo>

    </subChainChangeMoveSelector>

Note

If minimumSubChainSize is 1 (which is the default), this selector might select the same move as a ChangeMoveSelector, at a far lower selection probability (because each move type has the same selection chance by default (not every move instance) and there are far more SubChainChangeMove instances than ChangeMove instances). However, don't just remove the ChangeMoveSelector, because experiments show that it's good to focus on ChangeMoves.

Furthermore, in a SubChainSwapMoveSelector, setting minimumSubChainSize prevents swapping a subchain of size 1 with a subchain of at least size 2.

The property selectReversingMoveToo (defaults to true) enabled selecting the reverse of every subchain too.

7.2.6. subChainSwapMoveSelector

The subChainSwapMove selects 2 different subChains and moves it to another place in a different or the same anchor chain.


    <subChainSwapMoveSelector/>

The secondarySubChainSelector is rarely needed: if it is not specified, entities from the same subChainSelector are swapped.


    <subChainSwapMoveSelector>

      ... <!-- Normal selector properties -->

      <entityClass>...Customer</entityClass>

      <subChainSelector>

        <valueSelector>

          <variableName>previousStandstill</variableName>

          ...

        </valueSelector>

        <minimumSubChainSize>2</minimumSubChainSize>

        <maximumSubChainSize>40</maximumSubChainSize>

      </subChainSelector>

      <secondarySubChainSelector>

        <valueSelector>

          <variableName>previousStandstill</variableName>

          ...

        </valueSelector>

        <minimumSubChainSize>2</minimumSubChainSize>

        <maximumSubChainSize>40</maximumSubChainSize>

      </secondarySubChainSelector>

      <selectReversingMoveToo>true</selectReversingMoveToo>

    </subChainSwapMoveSelector>

The other properties are explained in subChainChangeMoveSelector.

7.3. Combining multiple `MoveSelector`s

7.3.1. unionMoveSelector

A unionMoveSelector selects a Move by selecting 1 of its child MoveSelectors to supply the next Move.


    <unionMoveSelector>

      <...MoveSelector/>

      <...MoveSelector/>

      <...MoveSelector/>

      ...

    </unionMoveSelector>

The selectorProbabilityWeightFactory determines in selectionOrder RANDOM how often a child MoveSelector is selected to supply the next Move. By default, each child MoveSelector has the same chance of being selected.


    <unionMoveSelector>

      ... <!-- Normal selector properties -->

      <selectorProbabilityWeightFactoryClass>...ProbabilityWeightFactory</selectorProbabilityWeightFactoryClass>

      <changeMoveSelector>

        <fixedProbabilityWeight>...</fixedProbabilityWeight>

        ...

      </changeMoveSelector>

      <swapMoveSelector>

        <fixedProbabilityWeight>...</fixedProbabilityWeight>

        ...

      </swapMoveSelector>

      <...MoveSelector>

        <fixedProbabilityWeight>...</fixedProbabilityWeight>

        ...

      </...MoveSelector>

      ...

    </unionMoveSelector>

Change the fixedProbabilityWeight of such a child to select it more often. For example, the unionMoveSelector can return a SwapMove twice as often as a ChangeMove:


    <unionMoveSelector>

      <changeMoveSelector>

        <fixedProbabilityWeight>1.0</fixedProbabilityWeight>

        ...

      </changeMoveSelector>

      <swapMoveSelector>

        <fixedProbabilityWeight>2.0</fixedProbabilityWeight>

        ...

      </swapMoveSelector>

    </unionMoveSelector>

The number of possible ChangeMoves is very different from the number of possible SwapMoves and furthermore it's problem dependent. To give each individual Move the same selection chance (as opposed to each MoveSelector), use the FairSelectorProbabilityWeightFactory:


    <unionMoveSelector>

      <selectorProbabilityWeightFactoryClass>org.optaplanner.core.impl.heuristic.selector.common.decorator.FairSelectorProbabilityWeightFactory</selectorProbabilityWeightFactoryClass>

      <changeMoveSelector/>

      <swapMoveSelector/>

    </unionMoveSelector>

7.3.2. cartesianProductMoveSelector

A cartesianProductMoveSelector selects a new CompositeMove. It builds that CompositeMove by selecting 1 Move per child MoveSelector and adding it to the CompositeMove.


    <cartesianProductMoveSelector>

      <...MoveSelector/>

      <...MoveSelector/>

      <...MoveSelector/>

      ...

    </cartesianProductMoveSelector>

The propery ignoreEmptyChildIterators (true by default) will ignore every empty childMoveSelector to avoid returning no moves. For example: a cartesian product of changeMoveSelector A and B, for which B is empty (because all it's entities are immovable) returns no moves if ignoreEmptyChildIterators is false and the moves of A if ignoreEmptyChildIterators is true.


    <cartesianProductMoveSelector>

      ... <!-- Normal selector properties -->

      <ignoreEmptyChildIterators>true</ignoreEmptyChildIterators>

      <changeMoveSelector>

        ...

      </changeMoveSelector>

      <swapMoveSelector>

        ...

      </swapMoveSelector>

      <...MoveSelector>

        ...

      </...MoveSelector>

      ...

    </cartesianProductMoveSelector>

7.4. EntitySelector


      <entitySelector/>

The entityClass property is only required if it cannot be deduced automatically because there are multiple entity classes.


      <entitySelector>

        ... <!-- Normal selector properties -->

        <entityClass>org.optaplanner.examples.curriculumcourse.domain.Lecture</entityClass>

      </entitySelector>

7.5. ValueSelector


      <valueSelector/>

The variableName property is only required if it cannot be deduced automatically because there are multiple variables (for the related entity class).


      <valueSelector>

        ... <!-- Normal selector properties -->

        <variableName>room</variableName>

      </valueSelector>

In exotic Construction Heuristic configurations, the entityClass from the EntitySelector sometimes needs to be downcasted, which can be done with the property downcastEntityClass:


      <valueSelector>

        <downcastEntityClass>...LeadingExam</downcastEntityClass>

        <variableName>period</variableName>

      </valueSelector>

If a selected entity cannot be downcasted, the ValueSelector is empty for that entity.

7.6. General `Selector` features

7.6.1. `CacheType`: Create moves ahead of time or Just In Time

A Selector's cacheType determines when a selection (such as a Move, an entity, a value, ...) is created and how long it lives.

Almost every Selector supports setting a cacheType:


    <changeMoveSelector>

      <cacheType>PHASE</cacheType>

      ...

    </changeMoveSelector>

The following cacheTypes are supported:

JUST_IN_TIME (default): Not cached. Construct each selection (Move, ...) just before it's used. This scales up well in memory footprint.
STEP: Cached. Create each selection (Move, ...) at the beginning of a step and cache them in a list for the remainder of the step. This scales up badly in memory footprint.
PHASE: Cached. Create each selection (Move, ...) at the beginning of a solver phase and cache them in a list for the remainder of the phase. Some selections cannot be phase cached because the list changes every step. This scales up badly in memory footprint, but has a slight performance gain.
SOLVER: Cached. Create each selection (Move, ...) at the beginning of a Solver and cache them in a list for the remainder of the Solver. Some selections cannot be solver cached because the list changes every step. This scales up badly in memory footprint, but has a slight performance gain.

A cacheType can be set on composite selectors too:


    <unionMoveSelector>

      <cacheType>PHASE</cacheType>

      <changeMoveSelector/>

      <swapMoveSelector/>

      ...

    </unionMoveSelector>

Nested selectors of a cached selector cannot be configured to be cached themselves, unless it's a higher cacheType. For example: a STEP cached unionMoveSelector can hold a PHASE cached changeMoveSelector, but not a STEP cached changeMoveSelector.

7.6.2. SelectionOrder: original, sorted, random, shuffled or probabilistic

A Selector's selectionOrder determines the order in which the selections (such as Moves, entities, values, ...) are iterated. An optimization algorithm will usually only iterate through a subset of its MoveSelector's selections, starting from the start, so the selectionOrder is critical to decide which Moves are actually evaluated.

Almost every Selector supports setting a selectionOrder:


    <changeMoveSelector>

      ...

      <selectionOrder>RANDOM</selectionOrder>

      ...

    </changeMoveSelector>

The following selectionOrders are supported:

ORIGINAL: Select the selections (Moves, entities, values, ...) in default order. Each selection will be selected only once.
- For example: A0, A1, A2, A3, ..., B0, B1, B2, B3, ..., C0, C1, C2, C3, ...
SORTED: Select the selections (Moves, entities, values, ...) in sorted order. Each selection will be selected only once. Requires cacheType >= STEP. Mostly used on an entitySelector or valueSelector for construction heuristics. See sorted selection.
- For example: A0, B0, C0, ..., A2, B2, C2, ..., A1, B1, C1, ...
RANDOM (default): Select the selections (Moves, entities, values, ...) in non-shuffled random order. A selection might be selected multiple times. This scales up well in performance because it does not require caching.
- For example: C2, A3, B1, C2, A0, C0, ...
SHUFFLED: Select the selections (Moves, entities, values, ...) in shuffled random order. Each selection will be selected only once. Requires cacheType >= STEP. This scales up badly in performance, not just because it requires caching, but also because a random number is generated for each element, even if it's not selected (which is the grand majority when scaling up).
- For example: C2, A3, B1, A0, C0, ...
PROBABILISTIC: Select the selections (Moves, entities, values, ...) in random order, based on the selection probability of each element. A selection with a higher probability has a higher chance to be selected than elements with a lower probability. A selection might be selected multiple times. Requires cacheType >= STEP. Mostly used on an entitySelector or valueSelector. See probabilistic selection.
- For example: B1, B1, A1, B2, B1, C2, B1, B1, ...

A selectionOrder can be set on composite selectors too.

Note

When a Selector is cached, all of its nested Selectors will naturally default to selectionOrder ORIGINAL. Avoid overwriting the selectionOrder of those nested Selectors.

7.6.3. Recommended combinations of `CacheType` and `SelectionOrder`

7.6.3.1. Just in time random selection (default)

This combination is great for big use cases (10 000 entities or more), as it scales up well in memory footprint and performance. Other combinations are often not even viable on such sizes. It works for smaller use cases too, so it's a good way to start out. It's the default, so this explicit configuration of cacheType and selectionOrder is actually obsolete:


    <unionMoveSelector>

      <cacheType>JUST_IN_TIME</cacheType>

      <selectionOrder>RANDOM</selectionOrder>



      <changeMoveSelector/>

      <swapMoveSelector/>

    </unionMoveSelector>

Here's how it works. When Iterator<Move>.next() is called, a child MoveSelector is randomly selected (1), which creates a random Move is created (2, 3, 4) and is then returned (5):

Notice that it never creates a list of Moves and it generates random numbers only for Moves that are actually selected.

7.6.3.2. Cached shuffled selection

This combination often wins for small and medium use cases (5000 entities or less). Beyond that size, it scales up badly in memory footprint and performance.


    <unionMoveSelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>SHUFFLED</selectionOrder>



      <changeMoveSelector/>

      <swapMoveSelector/>

    </unionMoveSelector>

Here's how it works: At the start of the phase (or step depending on the cacheType), all moves are created (1) and cached (2). When MoveSelector.iterator() is called, the moves are shuffled (3). When Iterator<Move>.next() is called, the next element in the shuffled list is returned (4):

Notice that each Move will only be selected once, even though they are selected in random order.

Use cacheType PHASE if none of the (possibly nested) Selectors require STEP. Otherwise, do something like this:


    <unionMoveSelector>

      <cacheType>STEP</cacheType>

      <selectionOrder>SHUFFLED</selectionOrder>



      <changeMoveSelector>

        <cacheType>PHASE</cacheType>

      </changeMoveSelector>

      <swapMoveSelector/>

        <cacheType>PHASE</cacheType>

      </swapMoveSelector>

      <pillarSwapMoveSelector/><!-- Does not support cacheType PHASE -->

    </unionMoveSelector>

7.6.3.3. Cached random selection

This combination is often a worthy competitor for medium use cases, especially with fast stepping optimization algorithms (such as simulated annealing). Unlike cached shuffled selection, it doesn't waste time shuffling the move list at the beginning of every step.


    <unionMoveSelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>RANDOM</selectionOrder>



      <changeMoveSelector/>

      <swapMoveSelector/>

    </unionMoveSelector>

7.6.4. Filtered selection

There can be certain moves that you don't want to select, because:

The move is pointless and would only waste CPU time. For example, swapping 2 lectures of the same course will result in the same score and the same schedule because all lectures of 1 course are interchangeable (same teacher, same students, same topic).
Doing the move would break a build-in hard constraint, so the solution would be infeasible but the score function doesn't check build-in hard constraints (for performance gain). For example, don't change a gym lecture to a room which is not a gym room.
- Note that any build-in hard constraint must usually be filtered on every move type. For example, also don't swap the room of a gym lecture with another lecture if the other lecture's original room isn't a gym room.

Filtered selection can happen on any Selector in the selector tree, including any MoveSelector, EntitySelector or ValueSelector. It works with any cacheType and selectionOrder.

Filtering uses the interface SelectionFilter:

public interface SelectionFilter<T> {


    boolean accept(ScoreDirector scoreDirector, T selection);


}

Implement the method accept to return false on a discarded selection. Unaccepted moves will not be selected and will therefore never have their method doMove called.

public class DifferentCourseSwapMoveFilter implements SelectionFilter<SwapMove> {


    public boolean accept(ScoreDirector scoreDirector, SwapMove move) {

        Lecture leftLecture = (Lecture) move.getLeftEntity();

        Lecture rightLecture = (Lecture) move.getRightEntity();

        return !leftLecture.getCourse().equals(rightLecture.getCourse());

    }


}

Apply the filter on the lowest level possible. In most cases, you 'll need to know both the entity and the value involved and you'll have to apply a filterClass on the moveSelector:


    <swapMoveSelector>

      <filterClass>org.optaplanner.examples.curriculumcourse.solver.move.DifferentCourseSwapMoveFilter</filterClass>

    </swapMoveSelector>

But if possible, apply it on a lower levels, such as a filterClass on the entitySelector or valueSelector:


    <changeMoveSelector>

      <entitySelector>

        <filterClass>...EntityFilter</filterClass>

      </entitySelector>

    </changeMoveSelector>

You can configure multiple filterClass elements on a single selector.

7.6.5. Sorted selection

Sorted selection can happen on any Selector in the selector tree, including any MoveSelector, EntitySelector or ValueSelector. It does not work with cacheType JUST_IN_TIME and it only works with selectionOrder SORTED.

It's mostly used in construction heuristics.

Note

If the choosen construction heuristic implies sorting, for example FIRST_FIT_DECREASING implies that the EntitySelector is sorted, there is no need to explicitly configure a Selector with sorting. If you do explicitely configure the Selector, it overwrites the default settings of that construction heuristic.

7.6.5.1. Sorted selection by `SorterManner`

Some Selector types implement a SorterManner out of the box:

EntitySelector supports:

DECREASING_DIFFICULTY: Sorts the planning entities according to decreasing planning entity difficulty. Requires that planning entity difficulty is annotated on the domain model.


    <entitySelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>SORTED</selectionOrder>

      <sorterManner>DECREASING_DIFFICULTY</sorterManner>

    </entitySelector>

ValueSelector supports:

INCREASING_STRENGTH: Sorts the planning values according to increasing planning value strength. Requires that planning value strength is annotated on the domain model.


    <valueSelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>SORTED</selectionOrder>

      <sorterManner>INCREASING_STRENGTH</sorterManner>

    </valueSelector>

7.6.5.2. Sorted selection by `Comparator`

An easy way to sort a Selector is with a plain old Comparator:

public class CloudProcessDifficultyComparator implements Comparator<CloudProcess> {


    public int compare(CloudProcess a, CloudProcess b) {

        return new CompareToBuilder()

                .append(a.getRequiredMultiplicand(), b.getRequiredMultiplicand())

                .append(a.getId(), b.getId())

                .toComparison();

    }


}

You 'll also need to configure it (unless it's annotated on the domain model and automatically applied by the optimization algorithm):


    <entitySelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>SORTED</selectionOrder>

      <sorterComparatorClass>...CloudProcessDifficultyComparator</sorterComparatorClass>

      <sorterOrder>DESCENDING</sorterOrder>

    </entitySelector>

7.6.5.3. Sorted selection by `SelectionSorterWeightFactory`

If you need the entire Solution to sort a Selector, use a SelectionSorterWeightFactory instead:

public interface SelectionSorterWeightFactory<Sol extends Solution, T> {


    Comparable createSorterWeight(Sol solution, T selection);


}

public class QueenDifficultyWeightFactory implements SelectionSorterWeightFactory<NQueens, Queen> {


    public Comparable createSorterWeight(NQueens nQueens, Queen queen) {

        int distanceFromMiddle = calculateDistanceFromMiddle(nQueens.getN(), queen.getColumnIndex());

        return new QueenDifficultyWeight(queen, distanceFromMiddle);

    }


    // ...


    public static class QueenDifficultyWeight implements Comparable<QueenDifficultyWeight> {


        private final Queen queen;

        private final int distanceFromMiddle;


        public QueenDifficultyWeight(Queen queen, int distanceFromMiddle) {

            this.queen = queen;

            this.distanceFromMiddle = distanceFromMiddle;

        }


        public int compareTo(QueenDifficultyWeight other) {

            return new CompareToBuilder()

                    // The more difficult queens have a lower distance to the middle

                    .append(other.distanceFromMiddle, distanceFromMiddle) // Decreasing

                    // Tie breaker

                    .append(queen.getColumnIndex(), other.queen.getColumnIndex())

                    .toComparison();

        }


    }


}

You 'll also need to configure it (unless it's annotated on the domain model and automatically applied by the optimization algorithm):


    <entitySelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>SORTED</selectionOrder>

      <sorterWeightFactoryClass>...QueenDifficultyWeightFactory</sorterWeightFactoryClass>

      <sorterOrder>DESCENDING</sorterOrder>

    </entitySelector>

7.6.5.4. Sorted selection by `SelectionSorter`

Alternatively, you can also use the interface SelectionSorter directly:

public interface SelectionSorter<T> {


    void sort(ScoreDirector scoreDirector, List<T> selectionList);


}


    <entitySelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>SORTED</selectionOrder>

      <sorterClass>...MyEntitySorter</sorterClass>

    </entitySelector>

7.6.6. Probabilistic selection

Probabilistic selection can happen on any Selector in the selector tree, including any MoveSelector, EntitySelector or ValueSelector. It does not work with cacheType JUST_IN_TIME and it only works with selectionOrder PROBABILISTIC.

Each selection has a probabilityWeight, which determines the chance that's that selection will be selected:

public interface SelectionProbabilityWeightFactory<T> {


    double createProbabilityWeight(ScoreDirector scoreDirector, T selection);


}


    <entitySelector>

      <cacheType>PHASE</cacheType>

      <selectionOrder>PROBABILISTIC</selectionOrder>

      <probabilityWeightFactoryClass>...MyEntityProbabilityWeightFactoryClass</probabilityWeightFactoryClass>

    </entitySelector>

For example, if there are 3 entities: process A (probabilityWeight 2.0), process B (probabilityWeight 0.5) and process C (probabilityWeight 0.5), then process A will be selected 4 times more than B and C.

7.6.7. Limited selection

Selecting all possible moves sometimes does not scale well enough, especially for construction heuristics (which don't support acceptedCountLimit).

To limit the number of selected selection per step, apply a selectedCountLimit on the selector:


    <changeMoveSelector>

      <selectedCountLimit>100</selectedCountLimit>

    </changeMoveSelector>

Note

To scale Local Search, setting acceptedCountLimit is usually better than using selectedCountLimit.

7.6.8. Mimic selection (record/replay)

During mimic selection, 1 normal selector records its selection and 1 or multiple other special selectors replay that selection. The recording selector acts as a normal selector and supports all other configuration properties. A replaying selector mimics the recording selection and support no other configuration properties.

The recording selector needs an id. A replaying selector must reference a recorder's id with a mimicSelectorRef:


      <cartesianProductMoveSelector>

        <changeMoveSelector>

          <entitySelector id="entitySelector"/>

          <valueSelector>

            <variableName>period</variableName>

          </valueSelector>

        </changeMoveSelector>

        <changeMoveSelector>

          <entitySelector mimicSelectorRef="entitySelector"/>

          <valueSelector>

            <variableName>room</variableName>

          </valueSelector>

        </changeMoveSelector>

      </cartesianProductMoveSelector>

Mimic selection is usefull to create a composite move from 2 moves that affect the same entity.

7.7. Custom moves

7.7.1. Which move types might be missing in my implementation?

To determine which move types might be missing in your implementation, run a Benchmarker for a short amount of time and configure it to write the best solutions to disk. Take a look at such a best solution: it will likely be a local optima. Try to figure out if there's a move that could get out of that local optima faster.

If you find one, implement that course-grained move, mix it with the existing moves and benchmark it against the previous configurations to see if you want to keep it.

7.7.2. Custom moves introduction

Instead of reusing the generic Moves (such as ChangeMove) you can also implement your own Moves. Generic and custom MoveSelectors can be combined as desired.

A custom Move can be tailored to work to the advantage of your constraints. For example, in examination scheduling, changing the period of an exam A also changes te period of all the exams that need to coincide with exam A.

A custom Move is also slightly faster than a generic Move. However, it's far more work to implement and much harder to avoid bugs. After implementing a custom Move, make sure to turn on environmentMode FULL_ASSERT to check for score corruptions.

7.7.3. The interface `Move`

Your custom moves must implement the Move interface:

public interface Move {


    boolean isMoveDoable(ScoreDirector scoreDirector);


    Move createUndoMove(ScoreDirector scoreDirector);

    void doMove(ScoreDirector scoreDirector);


    Collection<? extends Object> getPlanningEntities();

    Collection<? extends Object> getPlanningValues();


}

Let's take a look at the Move implementation for 4 queens which moves a queen to a different row:

public class RowChangeMove extends AbstractMove {


    private Queen queen;

    private Row toRow;


    public RowChangeMove(Queen queen, Row toRow) {

        this.queen = queen;

        this.toRow = toRow;

    }


    // ... see below


}

An instance of RowChangeMove moves a queen from its current row to a different row.

Planner calls the doMove(ScoreDirector) method to do a move. The Move implementation must notify the ScoreDirector of any changes it make to planning entity's variables:

    public void doMove(ScoreDirector scoreDirector) {

        scoreDirector.beforeVariableChanged(queen, "row"); // before changes are made to the queen.row

        queen.setRow(toRow);

        scoreDirector.afterVariableChanged(queen, "row"); // after changes are made to the queen.row

    }

You need to call the methods scoreDirector.beforeVariableChanged(Object, String) and scoreDirector.afterVariableChanged(Object, String) directly before and after modifying the entity.

Note

You can alter multiple entities in a single move and effectively create a big move (also known as a coarse-grained move).

Warning

A Move can only change/add/remove planning entities, it must not change any of the problem facts.

Planner automatically filters out non doable moves by calling the isMoveDoable(ScoreDirector) method on a move. A non doable move is:

A move that changes nothing on the current solution. For example, moving queen B0 to row 0 is not doable, because it is already there.
A move that is impossible to do on the current solution. For example, moving queen B0 to row 10 is not doable because it would move it outside the board limits.

In the n queens example, a move which moves the queen from its current row to the same row isn't doable:

    public boolean isMoveDoable(ScoreDirector scoreDirector) {

        return !ObjectUtils.equals(queen.getRow(), toRow);

    }

Because we won't generate a move which can move a queen outside the board limits, we don't need to check it. A move that is currently not doable could become doable on the working Solution of a later step.

Each move has an undo move: a move (normally of the same type) which does the exact opposite. In the example above the undo move of C0 to C2 would be the move C2 to C0. An undo move is created from a Move, before the Move has been done on the current solution.

    public Move createUndoMove(ScoreDirector scoreDirector) {

        return new RowChangeMove(queen, queen.getRow());

    }

Notice that if C0 would have already been moved to C2, the undo move would create the move C2 to C2, instead of the move C2 to C0.

A solver phase might do and undo the same Move more than once. In fact, many solver phases will iteratively do an undo a number of moves to evaluate them, before selecting one of those and doing that move again (without undoing it this time).

A Move must implement the getPlanningEntities() and getPlanningValues() methods. They are used by entity tabu and value tabu respectively. When they are called, the Move has already been done.

    public List<? extends Object> getPlanningEntities() {

        return Collections.singletonList(queen);

    }


    public Collection<? extends Object> getPlanningValues() {

        return Collections.singletonList(toRow);

    }

If your Move changes multiple planning entities, return all of them in getPlanningEntities() and return all their values (to which they are changing) in getPlanningValues().

    public Collection<? extends Object> getPlanningEntities() {

        return Arrays.asList(leftCloudProcess, rightCloudProcess);

    }


    public Collection<? extends Object> getPlanningValues() {

        return Arrays.asList(leftCloudProcess.getComputer(), rightCloudProcess.getComputer());

    }

A Move must implement the equals() and hashCode() methods. 2 moves which make the same change on a solution, should be equal.

    public boolean equals(Object o) {

        if (this == o) {

            return true;

        } else if (o instanceof RowChangeMove) {

            RowChangeMove other = (RowChangeMove) o;

            return new EqualsBuilder()

                    .append(queen, other.queen)

                    .append(toRow, other.toRow)

                    .isEquals();

        } else {

            return false;

        }

    }


    public int hashCode() {

        return new HashCodeBuilder()

                .append(queen)

                .append(toRow)

                .toHashCode();

    }

Notice that it checks if the other move is an instance of the same move type. This instanceof check is important because a move will be compared to a move with another move type if you're using more then 1 move type.

It's also recommended to implement the toString() method as it allows you to read Planner's logging more easily:

    public String toString() {

        return queen + " => " + toRow;

    }

Now that we can implement a single custom Move, let's take a look at generating such custom moves.

7.7.4. `MoveListFactory`: the easy way to generate custom moves

The easiest way to generate custom moves is by implementing the interface MoveListFactory:

public interface MoveListFactory<S extends Solution> {


    List<Move> createMoveList(S solution);


}

For example:

public class RowChangeMoveFactory implements MoveListFactory<NQueens> {


    public List<Move> createMoveList(NQueens nQueens) {

        List<Move> moveList = new ArrayList<Move>();

        for (Queen queen : nQueens.getQueenList()) {

            for (Row toRow : nQueens.getRowList()) {

                moveList.add(new RowChangeMove(queen, toRow));

            }

        }

        return moveList;

    }


}

Simple configuration (which can be nested in a unionMoveSelector just like any other MoveSelector):


    <moveListFactory>

      <moveListFactoryClass>org.optaplanner.examples.nqueens.solver.move.factory.RowChangeMoveFactory</moveListFactoryClass>

    </moveListFactory>

Because the MoveListFactory generates all moves at once in a List<Move>, it does not support cacheType JUST_IN_TIME. Therefore, moveListFactory uses cacheType STEP by default and it scales badly in memory footprint.


    <moveListFactory>

      ... <!-- Normal moveSelector properties -->

      <moveListFactoryClass>org.optaplanner.examples.nqueens.solver.move.factory.RowChangeMoveFactory</moveListFactoryClass>

    </moveListFactory>

7.7.5. `MoveIteratorFactory`: generate custom moves just in time

Use this advanced form to generate custom moves by implementing the interface MoveIteratorFactory:

public interface MoveIteratorFactory {


    long getSize(ScoreDirector scoreDirector);


    Iterator<Move> createOriginalMoveIterator(ScoreDirector scoreDirector);


    Iterator<Move> createRandomMoveIterator(ScoreDirector scoreDirector, Random workingRandom);


}

The method getSize() must give an estimation of the size. It doesn't need to be correct. The method createOriginalMoveIterator is called if the selectionOrder is ORIGINAL or if it is cached. The method createRandomMoveIterator is called for selectionOrder RANDOM combined with cacheType JUST_IN_TIME.

Important

Don't create a collection (list, array, map, set) of Moves when creating the Iterator<Move>: the whole purpose of MoveIteratorFactory over MoveListFactory is giving you the ability to create a Move just in time in the Iterator's method next().

Simple configuration (which can be nested in a unionMoveSelector just like any other MoveSelector):


    <moveIteratorFactory>

      <moveIteratorFactoryClass>...</moveIteratorFactoryClass>

    </moveIteratorFactory>

8.3. Branch And Bound


    <moveIteratorFactory>

      ... <!-- Normal moveSelector properties -->

      <moveIteratorFactoryClass>...</moveIteratorFactoryClass>

    </moveIteratorFactory>

Chapter 8. Exhaustive search

8.1. Overview

8.2. Brute Force

8.2.1. Algorithm description
8.2.2. Configuration

8.3.1. Algorithm description
8.3.2. Configuration

8.4. Scalability of Exhaustive Search

8.1. Overview

Exact methods will always find the global optimum and recognize it too. That being said, they don't scale (not even beyond toy data sets) and are therefore mostly useless.

8.2. Brute Force

8.2.1. Algorithm description

The Brute Force algorithm creates and evaluates every possible solution.

Notice that it creates a search tree that explodes exponentially as the problem size increases, so it hits a scalability wall.

Important

Brute Force is mostly unusable for a real-world problem due to time limitations, as shown in scalability of Exhaustive Search.

8.2.2. Configuration

Simplest configuration of Brute Force:


<solver>

  ...

  <exhaustiveSearch>

    <exhaustiveSearchType>BRUTE_FORCE</exhaustiveSearchType>

  </exhaustiveSearch>

</solver>

8.3. Branch And Bound

8.3.1. Algorithm description

Branch And Bound also explores nodes in an exponential search tree, but it investigates more promising nodes first and prunes away worthless nodes.

For each node, Branch And Bound calculates the optimistic bound: the best possible score to which that node can lead to. If the optimistic bound of a node is lower or equal to the global pessimistic bound, then it prunes away that node (including the entire branch of all its subnodes).

Note

Academic papers use the term lower bound instead of optimistic bound (and the term upper bound instead of pessimistic bound), because they minimize the score.

OptaPlanner maximizes the score (because it supports combining negative and positive constraints). Therefore, for clarity, OptaPlanner uses different terms, as it would be confusing to use the term lower bound for a bound which is always higher.

For example: at index 15, it can prune away all unvisited solutions with queen A on row 0, because none will be better than the solution of index 14 with a score of -1.

Notice that Branch And Bound (much like Brute Force) creates a search tree that explodes exponentially as the problem size increases. So it hits the same scalability wall, only a little bit later.

Important

Branch And Bound is mostly unusable for a real-world problem due to time limitations, as shown in scalability of Exhaustive Search.

8.3.2. Configuration

Simplest configuration of Branch And Bound:


<solver>

  ...

  <exhaustiveSearch>

    <exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>

  </exhaustiveSearch>

</solver>

Important

For the pruning to work with the default ScoreBounder, the InitializingScoreTrend should be set. Especially an InitializingScoreTrend of ONLY_DOWN (or at least has ONLY_DOWN in the leading score levels) prunes a lot.

Advanced configuration:


  <exhaustiveSearch>

    <exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>

    <nodeExplorationType>DEPTH_FIRST</nodeExplorationType>

    <entitySorterManner>DECREASING_DIFFICULTY_IF_AVAILABLE</entitySorterManner>

    <valueSorterManner>INCREASING_STRENGTH_IF_AVAILABLE</valueSorterManner>

  </exhaustiveSearch>

The nodeExplorationType options are:

DEPTH_FIRST (default): Explore deeper nodes first (and then a better score and then a better optimistic bound). Deeper nodes (especially leaf nodes) often improve the pessimistic bound. A better pessimistic bound allows pruning more nodes to reduce the search space.
```
  <exhaustiveSearch>

    <exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>

    <nodeExplorationType>DEPTH_FIRST</nodeExplorationType>

  </exhaustiveSearch>
```

BREADTH_FIRST (not recommended): Explore nodes layer by layer (and then a better score and then a better optimistic bound). Scales terribly in memory (and usually in performance too).


  <exhaustiveSearch>

    <exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>

    <nodeExplorationType>BREADTH_FIRST</nodeExplorationType>

  </exhaustiveSearch>

SCORE_FIRST: Explore nodes with a better score first (and then a better optimistic bound and then deeper nodes first). Might scale as terribly as BREADTH_FIRST in some cases.


  <exhaustiveSearch>

    <exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>

    <nodeExplorationType>SCORE_FIRST</nodeExplorationType>

  </exhaustiveSearch>

OPTIMISTIC_BOUND_FIRST: Explore nodes with a better optimistic bound first (and then a better score and then deeper nodes first). Might scale as terribly as BREADTH_FIRST in some cases.


  <exhaustiveSearch>

    <exhaustiveSearchType>BRANCH_AND_BOUND</exhaustiveSearchType>

    <nodeExplorationType>OPTIMISTIC_BOUND_FIRST</nodeExplorationType>

  </exhaustiveSearch>

The entitySorterManner options are:

DECREASING_DIFFICULTY: Initialize the more difficult planning entities first. This usually increases pruning (and therefore improves scalability). Requires the model to support planning entity difficulty comparison.
DECREASING_DIFFICULTY_IF_AVAILABLE (default): If the model supports planning entity difficulty comparison, behave like DECREASING_DIFFICULTY, else like NONE.
NONE: Initialize the planning entities in original order.

The valueSorterManner options are:

INCREASING_STRENGTH: Evaluate the planning values in increasing strength. Requires the model to support planning value strength comparison.
INCREASING_STRENGTH_IF_AVAILABLE (default): If the model supports planning value strength comparison, behave like INCREASING_STRENGTH, else like NONE.
DECREASING_STRENGTH: Evaluate the planning values in descreasing strength. Requires the model to support planning value strength comparison.
DECREASING_STRENGTH_IF_AVAILABLE (default): If the model supports planning value strength comparison, behave like DECREASING_STRENGTH, else like NONE.
NONE: Try the planning values in original order.

8.4. Scalability of Exhaustive Search

Exhaustive Search variants suffer from 2 big scalability issues:

They scale terribly memory wise.
They scale horribly performance wise.

As shown in these time spent graphs from the Benchmarker, Brute Force and Branch And Bound both hit a performance scalability wall. For example, on N queens it hits wall at a few dozen queens:

In most use cases, such as Cloud Balancing, the wall appears out of thin air:

Exhaustive Search hits this wall on small datasets already, so in production these optimizations algorithms are mostly useless. Use Construction Heuristics with Local Search instead: those can handle thousands of queens/computers easily.

Note

Throwing hardware at these scalability issues has no noticeable impact. Newer and more hardware are just a drop in the ocean. Moore's law cannot win against the onslaught of a few more planning entities in the dataset.

Chapter 9. Construction heuristics

9.1. Overview

A construction heuristic builds a pretty good initial solution in a finite length of time. Its solution isn't always feasible, but it finds it fast so metaheuristics can finish the job.

Construction heuristics terminate automatically, so there's usually no need to configure a Termination on the construction heuristic phase specifically.

9.2. First Fit

9.2.1. Algorithm description

The First Fit algorithm cycles through all the planning entities (in default order), initializing 1 planning entity at a time. It assigns the planning entity to the best available planning value, taking the already initialized planning entities into account. It terminates when all planning entities have been initialized. It never changes a planning entity after it has been assigned.

Notice that it starts with putting Queen A into row 0 (and never moving it later), which makes it impossible to reach the optimal solution. Suffixing this construction heuristic with metaheuristics can remedy that.

9.2.2. Configuration

Like First Fit, but assigns the more difficult planning entities first, because they are less likely to fit in the leftovers. So it sorts the planning entities on decreasing difficulty.


  <constructionHeuristic>

    <constructionHeuristicType>FIRST_FIT</constructionHeuristicType>

  </constructionHeuristic>

Note

If the InitializingScoreTrend is ONLY_DOWN, this algorithm is faster: for an entity, it picks the first move for which the score does not deteriorate the last step score, ignoring all subsequent moves.

For advanced configuration, see Allocate Entity From Queue.

9.3. First Fit Decreasing

9.3.1. Algorithm description

Requires the model to support planning entity difficulty comparison.

Note

One would expect that this algorithm has better results than First Fit. That's not always the case, but usually is.

9.3.2. Configuration

Like First Fit, but uses the weaker planning values first, because the strong planning values are more likely to be able to accommodate later planning entities. So it sorts the planning values on increasing strength.


  <constructionHeuristic>

    <constructionHeuristicType>FIRST_FIT_DECREASING</constructionHeuristicType>

  </constructionHeuristic>

Note

For advanced configuration, see Allocate Entity From Queue.

9.4. Weakest Fit

9.4.1. Algorithm description

Requires the model to support planning value strength comparison.

Note

Do not presume that this algorithm has better results than First Fit. That's often not the case.

9.4.2. Configuration

Combines First Fit Decreasing and Weakest Fit. So it sorts the planning entities on decreasing difficulty and the planning values on increasing strength.


  <constructionHeuristic>

    <constructionHeuristicType>WEAKEST_FIT</constructionHeuristicType>

  </constructionHeuristic>

Note

For advanced configuration, see Allocate Entity From Queue.

9.5. Weakest Fit Decreasing

9.5.1. Algorithm description

Requires the model to support planning entity difficulty comparison and planning value strength comparison.

Note

Do not presume that this algorithm has better results than First Fit, First Fit Decreasing and Weakest Fit. That's often not the case.

9.5.2. Configuration

Verbose simple configuration:


  <constructionHeuristic>

    <constructionHeuristicType>WEAKEST_FIT_DECREASING</constructionHeuristicType>

  </constructionHeuristic>

Note

For advanced configuration, see Allocate Entity From Queue.

9.6. Allocate Entity From Queue

9.6.1. Algorithm description

Allocate Entity From Queue is a versatile, generic form of First Fit, First Fit Decreasing, Weakest Fit and Weakest Fit Decreasing. It works like this:

Put all entities in a queue.
Assign the first entity (from that queue) to the best value.
Repeat until all entities are assigned.

9.6.2. Configuration

Simple configuration:


  <constructionHeuristic>

    <constructionHeuristicType>ALLOCATE_ENTITY_FROM_QUEUE</constructionHeuristicType>

  </constructionHeuristic>


  <constructionHeuristic>

    <constructionHeuristicType>ALLOCATE_ENTITY_FROM_QUEUE</constructionHeuristicType>

    <entitySorterManner>DECREASING_DIFFICULTY_IF_AVAILABLE</entitySorterManner>

    <valueSorterManner>INCREASING_STRENGTH_IF_AVAILABLE</valueSorterManner>

  </constructionHeuristic>

The entitySorterManner options are:

DECREASING_DIFFICULTY: Initialize the more difficult planning entities first. This usually increases pruning (and therefore improves scalability). Requires the model to support planning entity difficulty comparison.
DECREASING_DIFFICULTY_IF_AVAILABLE (default): If the model supports planning entity difficulty comparison, behave like DECREASING_DIFFICULTY, else like NONE.
NONE: Initialize the planning entities in original order.

The valueSorterManner options are:

INCREASING_STRENGTH: Evaluate the planning values in increasing strength. Requires the model to support planning value strength comparison.
INCREASING_STRENGTH_IF_AVAILABLE (default): If the model supports planning value strength comparison, behave like INCREASING_STRENGTH, else like NONE.
DECREASING_STRENGTH: Evaluate the planning values in descreasing strength. Requires the model to support planning value strength comparison.
DECREASING_STRENGTH_IF_AVAILABLE (default): If the model supports planning value strength comparison, behave like DECREASING_STRENGTH, else like NONE.
NONE: Try the planning values in original order.

Advanced detailed configuration. For example, a Best Fit Decreasing configuration for a single entity class with a single variable:


  <constructionHeuristic>

    <queuedEntityPlacer>

      <entitySelector id="placerEntitySelector">

        <cacheType>PHASE</cacheType>

        <selectionOrder>SORTED</selectionOrder>

        <sorterManner>DECREASING_DIFFICULTY</sorterManner>

      </entitySelector>

      <changeMoveSelector>

        <entitySelector mimicSelectorRef="placerEntitySelector"/>

        <valueSelector>

          <cacheType>PHASE</cacheType>

          <selectionOrder>SORTED</selectionOrder>

          <sorterManner>INCREASING_STRENGTH</sorterManner>

        </valueSelector>

      </changeMoveSelector>

    </queuedEntityPlacer>

  </constructionHeuristic>

Per step, the QueuedEntityPlacer selects 1 uninitialized entity from the EntitySelector and applies the winning Move (out of all the moves for that entity generated by the MoveSelector). The mimic selection ensures that the winning Move changes (only) the selected entity.

To customize the entity or value sorting, see sorted selection. Other Selector customization (such as filtering and limiting) is supported too.

9.6.3. Multiple variables

There are 2 ways to deal with multiple variables, depending on how their ChangeMoves are combined:

Cartesian product of the ChangeMoves (default): All variables of the selected entity are assigned together. Has far better results (especially for timetabling use cases).
Sequential ChangeMoves: One variable is assigned at a time. Scales much better, especially for 3 or more variables.

For example, presume a course scheduling example with 200 rooms and 40 periods.

This First Fit configuration for a single entity class with 2 variables, using a cartesian product of their ChangeMoves, will select 8000 moves per entity:


  <constructionHeuristic>

    <queuedEntityPlacer>

      <entitySelector id="placerEntitySelector">

        <cacheType>PHASE</cacheType>

      </entitySelector>

      <cartesianProductMoveSelector>

        <changeMoveSelector>

          <entitySelector mimicSelectorRef="placerEntitySelector"/>

          <valueSelector>

            <variableName>room</variableName>

          </valueSelector>

        </changeMoveSelector>

        <changeMoveSelector>

          <entitySelector mimicSelectorRef="placerEntitySelector"/>

          <valueSelector>

            <variableName>period</variableName>

          </valueSelector>

        </changeMoveSelector>

      </cartesianProductMoveSelector>

    </queuedEntityPlacer>

    ...

  </constructionHeuristic>

Warning

With 3 variables of 1000 values each, a cartesian product selects 1000000000 values per entity, which will take far too long.

This First Fit configuration for a single entity class with 2 variables, using sequential ChangeMoves, will select 240 moves per entity:


  <constructionHeuristic>

    <queuedEntityPlacer>

      <entitySelector id="placerEntitySelector">

        <cacheType>PHASE</cacheType>

      </entitySelector>

      <changeMoveSelector>

        <entitySelector mimicSelectorRef="placerEntitySelector"/>

        <valueSelector>

          <variableName>period</variableName>

        </valueSelector>

      </changeMoveSelector>

      <changeMoveSelector>

        <entitySelector mimicSelectorRef="placerEntitySelector"/>

        <valueSelector>

          <variableName>room</variableName>

        </valueSelector>

      </changeMoveSelector>

    </queuedEntityPlacer>

    ...

  </constructionHeuristic>

Important

Especially for sequential ChangeMoves, the order of the variables is important. In the example above, it's better to select the period first (instead of the other way around), because there are more hard constraints that do not involve the room (for example: no teacher should teach 2 lectures at the same time). Let the Benchmarker guide you.

With 3 or more variables, it's possible to combine the cartesian product and sequential techniques:


  <constructionHeuristic>

    <queuedEntityPlacer>

      ...

      <cartesianProductMoveSelector>

        <changeMoveSelector>...</changeMoveSelector>

        <changeMoveSelector>...</changeMoveSelector>

      </cartesianProductMoveSelector>

      <changeMoveSelector>...</changeMoveSelector>

    </queuedEntityPlacer>

    ...

  </constructionHeuristic>

9.6.4. Multiple entity classes

The easiest way to deal with multiple entity classes is to run a separate construction heuristic for each entity class:


  <constructionHeuristic>

    <queuedEntityPlacer>

      <entitySelector id="placerEntitySelector">

        <cacheType>PHASE</cacheType>

        <entityClass>...DogEntity</entityClass>

      </entitySelector>

      <changeMoveSelector>

        <entitySelector mimicSelectorRef="placerEntitySelector"/>

      </changeMoveSelector>

    </queuedEntityPlacer>

    ...

  </constructionHeuristic>

  <constructionHeuristic>

    <queuedEntityPlacer>

      <entitySelector id="placerEntitySelector">

        <cacheType>PHASE</cacheType>

        <entityClass>...CatEntity</entityClass>

      </entitySelector>

      <changeMoveSelector>

        <entitySelector mimicSelectorRef="placerEntitySelector"/>

      </changeMoveSelector>

    </queuedEntityPlacer>

    ...

  </constructionHeuristic>

9.6.5. Pick early type

There are several pick early types for Construction Heuristics:

NEVER: Evaluate all the selected moves to initialize the variable(s). This is the default if the InitializingScoreTrend is not ONLY_DOWN.


  <constructionHeuristic>

    ...

    <forager>

      <pickEarlyType>NEVER</pickEarlyType>

    </forager>

  </constructionHeuristic>

FIRST_NON_DETERIORATING_SCORE: Initialize the variable(s) with the first move that doesn't deteriorate the score, ignore the remaining selected moves. This is the default if the InitializingScoreTrend is ONLY_DOWN.
```
  <constructionHeuristic>

    ...

    <forager>

      <pickEarlyType>FIRST_NON_DETERIORATING_SCORE</pickEarlyType>

    </forager>

  </constructionHeuristic>
```
Note
If there are only negative constraints, but the InitializingScoreTrend is strictly not ONLY_DOWN, it can sometimes make sense to apply FIRST_NON_DETERIORATING_SCORE. Use the Benchmarker to decide if the score quality loss is worth the time gain.
FIRST_FEASIBLE_SCORE: Initialize the variable(s) with the first move that has a feasible score.
```
  <constructionHeuristic>

    ...

    <forager>

      <pickEarlyType>FIRST_FEASIBLE_SCORE</pickEarlyType>

    </forager>

  </constructionHeuristic>
```
If the InitializingScoreTrend is ONLY_DOWN, use FIRST_FEASIBLE_SCORE_OR_NON_DETERIORATING_HARD instead, because that's faster without any disadvantages.

FIRST_FEASIBLE_SCORE_OR_NON_DETERIORATING_HARD: Initialize the variable(s) with the first move that doesn't deteriorate the feasibility of the score any further.


  <constructionHeuristic>

    ...

    <forager>

      <pickEarlyType>FIRST_FEASIBLE_SCORE_OR_NON_DETERIORATING_HARD</pickEarlyType>

    </forager>

  </constructionHeuristic>

9.7. Allocate To Value From Queue

9.7.1. Algorithm description

Allocate To Value From Queue is a versatile, generic form of Nearest Neighbour. It works like this:

Put all values in a round-robin queue.
Assign the best entity to the first value (from that queue).
Repeat until all entities are assigned.

9.7.2. Configuration

Not yet implemented.

9.8. Cheapest Insertion

9.8.1. Algorithm description

The Cheapest Insertion algorithm cycles through all the planning values for all the planning entities, initializing 1 planning entity at a time. It assigns a planning entity to the best available planning value (out of all the planning entities and values), taking the already initialized planning entities into account. It terminates when all planning entities have been initialized. It never changes a planning entity after it has been assigned.

Note

Cheapest Insertion scales considerably worse than First Fit, etc.

9.8.2. Configuration

Simplest configuration of Cheapest Insertion:


  <constructionHeuristic>

    <constructionHeuristicType>CHEAPEST_INSERTION</constructionHeuristicType>

  </constructionHeuristic>

Note

For advanced configuration, see Allocate from pool.

9.9. Regret Insertion

9.9.1. Algorithm description

TODO

9.9.2. Configuration

TODO Not implemented yet.

9.10. Allocate From Pool

9.10.1. Algorithm description

Allocate From Pool is a versatile, generic form of Cheapest Insertion and Regret Insertion. It works like this:

Put all entity-value combinations in a pool.
Assign the best entity to best value.
Repeat until all entities are assigned.

9.10.2. Configuration

TODO

Chapter 10. Local search

10.1. Overview

Local Search starts from an initial solution and evolves that single solution into a mostly better and better solution. It uses a single search path of solutions, not a search tree. At each solution in this path it evaluates a number of moves on the solution and applies the most suitable move to take the step to the next solution. It does that for a high number of iterations until it's terminated (usually because its time has run out).

Local Search acts a lot like a human planner: it uses a single search path and moves facts around to find a good feasible solution. Therefore it's pretty natural to implement.

Local Search usually needs to start from an initialized solution, therefore it's usually required to configure a construction heuristic solver phase before it.

10.2. Local Search concepts

10.2.1. Taking steps

A step is the winning Move. The local search solver tries every move on the current solution and picks the best accepted move as the step:

Figure 10.1. Decide the next step at step 0 (4 queens example)

Because the move B0 to B3 has the highest score (-3), it is picked as the next step. If multiple moves have the same highest score, one is picked randomly, in this case B0 to B3. Note that C0 to C3 (not shown) could also have been picked because it also has the score -3.

The step is applied on the solution. From that new solution, the local search solver tries every move again, to decide the next step after that. It continually does this in a loop, and we get something like this:

Figure 10.2. All steps (4 queens example)

Notice that the local search solver doesn't use a search tree, but a search path. The search path is highlighted by the green arrows. At each step it tries all possible moves, but unless it's the step, it doesn't investigate that solution further. This is one of the reasons why local search is very scalable.

As you can see, Local Search solves the 4 queens problem by starting with the starting solution and make the following steps sequentially:

B0 to B3
D0 to B2
A0 to B1

If we turn on debug logging for the category org.optaplanner, then those steps are shown into the log:

INFO  Solving started: time spent (0), best score (-6), random (JDK with seed 0).
DEBUG     LS step (0), time spent (20), score (-3), new best score (-3), accepted/selected move count (12/12), picked move (col1@row0 => row3).
DEBUG     LS step (1), time spent (31), score (-1), new best score (-1), accepted/selected move count (12/12), picked move (col0@row0 => row1).
DEBUG     LS step (2), time spent (40), score (0), new best score (0), accepted/selected move count (12/12), picked move (col3@row0 => row2).
INFO  Local Search phase (0) ended: step total (3), time spent (41), best score (0).
INFO  Solving ended: time spent (41), best score (0), average calculate count per second (1780).

Notice that the logging uses the toString() method of the Move implementation: col1@row0 => row3.

A naive Local Search configuration solves the 4 queens problem in 3 steps, by evaluating only 37 possible solutions (3 steps with 12 moves each + 1 starting solution), which is only fraction of all 256 possible solutions. It solves 16 queens in 31 steps, by evaluating only 7441 out of 18446744073709551616 possible solutions. Note: with construction heuristics it's even a lot more efficient.

10.2.2. Deciding the next step

The local search solver decides the next step with the aid of 3 configurable components:

A MoveSelector which selects the possible moves of the current solution. See the chapter move and neighborhood selection.
An Acceptor which filters out unacceptable moves.
A Forager which gathers accepted moves and picks the next step from them.

The solver phase configuration looks like this:


  <localSearch>

    <unionMoveSelector>

      ...

    </unionMoveSelector>

    <acceptor>

      ...

    </acceptor>

    <forager>

      ...

    </forager>

  </localSearch>

In the example below, the MoveSelector generated the moves shown with the blue lines, the Acceptor accepted all of them and the Forager picked the move B0 to B3.

Turn on trace logging to show the decision making in the log:

INFO  Solver started: time spent (0), score (-6), new best score (-6), random (JDK with seed 0).
TRACE         Move index (0) not doable, ignoring move (col0@row0 => row0).
TRACE         Move index (1), score (-4), accepted (true), move (col0@row0 => row1).
TRACE         Move index (2), score (-4), accepted (true), move (col0@row0 => row2).
TRACE         Move index (3), score (-4), accepted (true), move (col0@row0 => row3).
...
TRACE         Move index (6), score (-3), accepted (true), move (col1@row0 => row3).
...
TRACE         Move index (9), score (-3), accepted (true), move (col2@row0 => row3).
...
TRACE         Move index (12), score (-4), accepted (true), move (col3@row0 => row3).
DEBUG     LS step (0), time spent (6), score (-3), new best score (-3), accepted/selected move count (12/12), picked move (col1@row0 => row3).
...

Because the last solution can degrade (for example in Tabu Search), the Solver remembers the best solution it has encountered through the entire search path. Each time the current solution is better than the last best solution, the current solution is cloned and referenced as the new best solution.

10.2.3. Acceptor

An Acceptor is used (together with a Forager) to active Tabu Search, Simulated Annealing, Late Acceptance, ... For each move it checks whether it is accepted or not.

By changing a few lines of configuration, you can easily switch from Tabu Search to Simulated Annealing or Late Acceptance and back.

You can implement your own Acceptor, but the build-in acceptors should suffice for most needs. You can also combine multiple acceptors.

10.2.4. Forager

A Forager gathers all accepted moves and picks the move which is the next step. Normally it picks the accepted move with the highest score. If several accepted moves have the highest score, one is picked randomly.

You can implement your own Forager, but the build-in forager should suffice for most needs.

10.2.4.1. Accepted count limit

When there are many possible moves, it becomes inefficient to evaluate all of them at every step. To evaluate only a random subset of all the moves, use:

An acceptedCountLimit integer, which specifies how many accepted moves should be evaluated during each step. By default, all accepted moves are evaluated at every step.
```
  <forager>

    <acceptedCountLimit>1000</acceptedCountLimit>

  </forager>
```

Unlike the n queens problem, real world problems require the use of acceptedCountLimit. Start from an acceptedCountLimit that takes a step in less then 2 seconds. Turn on INFO logging to see the step times. Use the Benchmarker to tweak the value.

Important

With a low acceptedCountLimit it is recommended to avoid using selectionOrder SHUFFLED because the shuffling generates a random number for every element in the selector, taking up a lot of time, but only a few elements are actually selected.

10.2.4.2. Pick early type

A forager can pick a move early during a step, ignoring subsequent selected moves. There are 3 pick early types for Local Search:

NEVER: A move is never picked early: all accepted moves are evaluated that the selection allows. This is the default.
```
    <forager>

      <pickEarlyType>NEVER</pickEarlyType>

    </forager>
```
FIRST_BEST_SCORE_IMPROVING: Pick the first accepted move that improves the best score. If none improve the best score, it behaves exactly like the pickEarlyType NEVER.
```
    <forager>

      <pickEarlyType>FIRST_BEST_SCORE_IMPROVING</pickEarlyType>

    </forager>
```
FIRST_LAST_STEP_SCORE_IMPROVING: Pick the first accepted move that improves the last step score. If none improve the last step score, it behaves exactly like the pickEarlyType NEVER.
```
    <forager>

      <pickEarlyType>FIRST_LAST_STEP_SCORE_IMPROVING</pickEarlyType>

    </forager>
```

10.3. Hill Climbing (Simple Local Search)

10.3.1. Algorithm description

Hill Climbing tries all selected moves and then takes the best move, which is the move which leads to the solution with the highest score. That best move is called the step move. From that new solution, it again tries all selected moves and takes the best move and continues like that iteratively. If multiple selected moves tie for the best move, one of them is randomly chosen as the best move.

Notice that one a queen has moved, it can be moved again later. This is a good thing, because in an NP-complete problem it's impossible to predict what will be the optimal final value for a planning variable.

10.3.2. Getting stuck in local optima

Hill Climbing always takes improving moves. This may seem like a good thing, but it's not: Hill Climbing can easily get stuck in a local optimum. This happens when it reaches a solution for which all the moves deteriorate the score. Even if it picks one of those moves, the next step might go back to the original solution and which case chasing it's own tail:

Improvements upon Hill Climbing (such as Tabu Search, Simulated Annealing and Late Acceptance) address the problem of being stuck in local optima. Therefore, it's recommend to never use Hill Climbing, unless you're absolutely sure there are no local optima in your planning problem.

10.3.3. Configuration