JBoss.orgCommunity Documentation

OptaPlanner User Guide

Version 6.2.0.CR1


1. OptaPlanner introduction
1.1. What is OptaPlanner?
1.2. What is a planning problem?
1.2.1. A planning problem is NP-complete or NP-hard
1.2.2. A planning problem has (hard and soft) constraints
1.2.3. A planning problem has a huge search space
1.3. Download and run the examples
1.3.1. Get the release zip and run the examples
1.3.2. Run the examples in an IDE (IntelliJ, Eclipse, NetBeans)
1.3.3. Use OptaPlanner with Maven, Gradle, Ivy, Buildr or ANT
1.3.4. Build OptaPlanner from source
1.4. Status of OptaPlanner
1.5. Compatibility
1.6. Relationship with Drools and jBPM
1.7. Questions, issues and blog
2. Quick start
2.1. Cloud balancing tutorial
2.1.1. Problem statement
2.1.2. Problem size
2.1.3. Domain model diagram
2.1.4. Main method
2.1.5. Solver configuration
2.1.6. Domain model implementation
2.1.7. Score configuration
2.1.8. Beyond this tutorial
3. Use cases and examples
3.1. Examples overview
3.2. Basic examples
3.2.1. N queens
3.2.2. Cloud balancing
3.2.3. Traveling salesman (TSP - Traveling salesman problem)
3.2.4. Dinner party
3.2.5. Tennis club scheduling
3.3. Real examples
3.3.1. Course timetabling (ITC 2007 track 3 - Curriculum course scheduling)
3.3.2. Machine reassignment (Google ROADEF 2012)
3.3.3. Vehicle routing
3.3.4. Project job scheduling
3.3.5. Hospital bed planning (PAS - Patient admission scheduling)
3.4. Difficult examples
3.4.1. Exam timetabling (ITC 2007 track 1 - Examination)
3.4.2. Employee rostering (INRC 2010 - Nurse rostering)
3.4.3. Traveling tournament problem (TTP)
3.4.4. Cheap time scheduling
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
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
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
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
8. Exhaustive search
8.1. Overview
8.2. Brute Force
8.2.1. Algorithm description
8.2.2. Configuration
8.3. Branch And Bound
8.3.1. Algorithm description
8.3.2. Configuration
8.4. Scalability of Exhaustive Search
9. Construction heuristics
9.1. Overview
9.2. First Fit
9.2.1. Algorithm description
9.2.2. Configuration
9.3. First Fit Decreasing
9.3.1. Algorithm description
9.3.2. Configuration
9.4. Weakest Fit
9.4.1. Algorithm description
9.4.2. Configuration
9.5. Weakest Fit Decreasing
9.5.1. Algorithm description
9.5.2. Configuration
9.6. Allocate Entity From Queue
9.6.1. Algorithm description
9.6.2. Configuration
9.6.3. Multiple variables
9.6.4. Multiple entity classes
9.6.5. Pick early type
9.7. Allocate To Value From Queue
9.7.1. Algorithm description
9.7.2. Configuration
9.8. Cheapest Insertion
9.8.1. Algorithm description
9.8.2. Configuration
9.9. Regret Insertion
9.9.1. Algorithm description
9.9.2. Configuration
9.10. Allocate From Pool
9.10.1. Algorithm description
9.10.2. Configuration
10. Local search
10.1. Overview
10.2. Local Search concepts
10.2.1. Taking steps
10.2.2. Deciding the next step
10.2.3. Acceptor
10.2.4. Forager
10.3. Hill Climbing (Simple Local Search)
10.3.1. Algorithm description
10.3.2. Getting stuck in local optima
10.3.3. Configuration
10.4. Tabu Search
10.4.1. Algorithm description
10.4.2. Configuration
10.5. Simulated Annealing
10.5.1. Algorithm description
10.5.2. Configuration
10.6. Late Acceptance
10.6.1. Algorithm description
10.6.2. Configuration
10.7. Step Counting Hill Climbing
10.7.1. Algorithm description
10.7.2. Configuration
10.8. Strategic Oscillation
10.8.1. Algorithm description
10.8.2. Configuration
10.9. Using a custom Termination, MoveSelector, EntitySelector, ValueSelector or Acceptor
11. Evolutionary algorithms
11.1. Overview
11.2. Evolutionary Strategies
11.3. Genetic Algorithms
12. Hyperheuristics
12.1. Overview
13. Partitioned search
13.1. Overview
14. Benchmarking and tweaking
14.1. Finding the best Solver configuration
14.2. Doing a benchmark
14.2.1. Adding a dependency on optaplanner-benchmark
14.2.2. Building and running a PlannerBenchmark
14.2.3. SolutionFileIO: input and output of Solution files
14.2.4. Warming up the HotSpot compiler
14.2.5. Benchmark blueprint: a predefined configuration
14.2.6. Writing the output solution of the benchmark runs
14.2.7. Benchmark logging
14.3. Benchmark report
14.3.1. HTML report
14.3.2. Ranking the Solvers
14.4. Summary statistics
14.4.1. Best score summary (graph and table)
14.4.2. Best score scalability summary (graph)
14.4.3. Winning score difference summary (graph and table)
14.4.4. Worst score difference percentage (ROI) summary (graph and table)
14.4.5. Average calculation count summary (graph and table)
14.4.6. Time spent summary (graph and table)
14.4.7. Time spent scalability summary (graph)
14.4.8. Best score per time spent summary (graph)
14.5. Statistic per dataset (graph and CSV)
14.5.1. Enabling a problem statistic
14.5.2. Best score over time statistic (graph and CSV)
14.5.3. Step score over time statistic (graph and CSV)
14.5.4. Calculate count per second statistic (graph and CSV)
14.5.5. Best solution mutation over time statistic (graph and CSV)
14.5.6. Move count per step statistic (graph and CSV)
14.5.7. Memory use statistic (graph and CSV)
14.6. Statistic per single benchmark (graph and CSV)
14.6.1. Enabling a single statistic
14.6.2. Constraint match total best score over time statistic (graph and CSV)
14.6.3. Constraint match total step score over time statistic (graph and CSV)
14.6.4. Picked move type best score diff over time statistic (graph and CSV)
14.6.5. Picked move type step score diff over time statistic (graph and CSV)
14.7. Advanced benchmarking
14.7.1. Benchmarking performance tricks
14.7.2. Template based benchmarking and matrix benchmarking
14.7.3. Benchmark report aggregation
15. Repeated planning
15.1. Introduction to repeated planning
15.2. Backup planning
15.3. Continuous planning (windowed planning)
15.3.1. Immovable planning entities
15.4. Real-time planning (event based planning)
15.4.1. ProblemFactChange
15.4.2. Daemon: solve() does not return
16. Integration
16.1. Overview
16.2. Persistent storage
16.2.1. Database: JPA and Hibernate
16.2.2. XML: XStream
16.2.3. XML: JAXB
16.3. SOA and ESB
16.3.1. Camel and Karaf
16.4. Other environments
16.4.1. JBoss modules, WildFly and JBoss EAP
16.4.2. OSGi
16.4.3. Android
16.5. Integration with human planners (politics)

OptaPlanner is a lightweight, embeddable planning engine that optimizes planning problems. It solves use cases, such as:

  • Employee shift rostering: timetabling nurses, repairmen, ...

  • Agenda scheduling: scheduling meetings, appointments, maintenance jobs, advertisements, ...

  • Educational timetabling: scheduling lessons, courses, exams, conference presentations, ...

  • Vehicle routing: planning vehicles (trucks, trains, boats, airplanes, ...) with freight and/or people

  • Bin packing: filling containers, trucks, ships and storage warehouses, but also cloud computers nodes, ...

  • Job shop scheduling: planning car assembly lines, machine queue planning, workforce task planning, ...

  • Cutting stock: minimizing waste while cutting paper, steel, carpet, ...

  • Sport scheduling: planning football leagues, baseball leagues, ...

  • Financial optimization: investment portfolio optimization, risk spreading, ...

Every organization faces planning problems: provide products or services with a limited set of constrained resources (employees, assets, time and money). OptaPlanner optimizes such planning to do more business with less resources. This is known as Constraint Satisfaction Programming (which is part of the discipline Operations Research).

OptaPlanner helps normal JavaTM programmers solve constraint satisfaction problems efficiently. Under the hood, it combines optimization heuristics and metaheuristics with very efficient score calculation.

OptaPlanner is open source software, released under the Apache Software License 2.0. This license is very liberal and allows reuse for commercial purposes. Read the layman's explanation. OptaPlanner is 100% pure JavaTM, runs on any JVM and is available in the Maven Central Repository too.

All the use cases above are probably NP-complete or harder. In layman's terms, NP-complete means:

  • It's easy to verify a given solution to a problem in reasonable time.

  • There is no silver bullet to find the optimal solution of a problem in reasonable time (*).

Note

(*) At least, none of the smartest computer scientists in the world have found such a silver bullet yet. But if they find one for 1 NP-complete problem, it will work for every NP-complete problem.

In fact, there's a $ 1,000,000 reward for anyone that proves if such a silver bullet actually exists or not.

The implication of this is pretty dire: solving your problem is probably harder than you anticipated, because the 2 common techniques won't suffice:

  • A brute force algorithm (even a smarter variant) will take too long.

  • A quick algorithm, for example in bin packing, putting in the largest items first, will return a solution that is far from optimal.

By using advanced optimization algorithms, OptaPlanner does find a good solution in reasonable time for such planning problems.

A planning problem has a number of solutions. There are several categories of solutions:

Counterintuitively, the number of possible solutions is huge (if calculated correctly), even with a small dataset. As you can see in the examples, most instances have a lot more possible solutions than the minimal number of atoms in the known universe (10^80). Because there is no silver bullet to find the optimal solution, any implementation is forced to evaluate at least a subset of all those possible solutions.

OptaPlanner supports several optimization algorithms to efficiently wade through that incredibly large number of possible solutions. Depending on the use case, some optimization algorithms perform better than others, but it's impossible to tell in advance. With OptaPlanner, it is easy to switch the optimization algorithm, by changing the solver configuration in a few lines of XML or code.

To try it now:

The Examples GUI application will open. Just pick an example:

Note

OptaPlanner itself has no GUI dependencies. It runs just as well on a server or a mobile JVM as it does on the desktop.

The OptaPlanner jars are also available in the central maven repository (and also in the JBoss maven repository).

If you use Maven, add a dependency to optaplanner-core in your project's pom.xml:


    <dependency>
      <groupId>org.optaplanner</groupId>
      <artifactId>optaplanner-core</artifactId>
    </dependency>

This is similar for Gradle, Ivy and Buildr. To identify the latest version, check the central maven repository.

Because you might end up using other optaplanner modules too, it's recommended to import the optaplanner-bom in Maven's dependencyManagement so the optaplanner version is specified only once:


  <dependencyManagement>
    <dependencies>
      <dependency>
        <groupId>org.optaplanner</groupId>
        <artifactId>optaplanner-bom</artifactId>
        <type>pom</type>
        <version>...</version>
        <scope>import</scope>
      </dependency>
      ...
    </dependencies>
  </dependencyManagement>

If you're still using ANT (without Ivy), copy all the jars from the download zip's binaries directory and manually verify that your classpath doesn't contain duplicate jars.

Note

The download zip's binaries directory contains far more jars then optaplanner-core actually uses. It also contains the jars used by other modules, such as optaplanner-benchmark.

Check the maven repository pom.xml files to determine the minimal dependency set for a specific version of a specific module.

It's easy to build OptaPlanner from source:

  1. Set up Git and clone optaplanner from GitHub (or alternatively, download the zipball):

    $ git clone git@github.com:droolsjbpm/optaplanner.git optaplanner
    ...

    Note

    If you don't have a GitHub account or your local Git installation isn't configured with it, use this command instead, to avoid an authentication issue:

    $ git clone https://github.com/droolsjbpm/optaplanner.git optaplanner
    ...
  2. Build it with Maven:

    $ cd optaplanner
    $ mvn clean install -DskipTests
    ...

    Note

    The first time, Maven might take a lot time, because it needs to download jars.

  3. Run the examples:

    $ cd optaplanner-examples
    $ mvn exec:exec
    ...
  4. Edit the sources in your favorite IDE.

  5. Optional: use a Java profiler.

OptaPlanner is:

OptaPlanner has a public API:

Note

This documentation covers some impl classes too. Those documented impl classes are reliable and safe to use (unless explicitly marked as experimental in this documentation), but we're just entirely comfortable yet to write their signatures in stone.

OptaPlanner is part of the KIE group of projects. It releases regularly (often once or twice per month) together with the Drools rule engine and the jBPM workflow engine.

See the architecture overview to learn more about the optional integration with Drools.

Questions and suggestions are welcome on our forum. Report any issue (such as a bug, improvement or a new feature request) for the OptaPlanner code or for this manual in our issue tracker.

Pull requests are very welcome and get priority treatment! By open sourcing your improvements, you 'll benefit from our peer review and from our improvements made upon your improvements.

Check our blog, Google+ (OptaPlanner, Geoffrey De Smet) and twitter (OptaPlanner, Geoffrey De Smet) for news and articles. If OptaPlanner helps you, help us by blogging or tweeting about it!

Suppose your company owns a number of cloud computers and needs to run a number of processes on those computers. Assign each process to a computer under the following 4 constraints.

Hard constraints which must be fulfilled:

Soft constraints which should be optimized:

How would you do that? This problem is a form of bin packing. Here's a simplified example where we assign 4 processes to 2 computers with 2 constraints (CPU and RAM) with a simple algorithm:

The simple algorithm used here is the First Fit Decreasing algorithm, which assigns the bigger processes first and assigns the smaller processes to the remaining space. As you can see, it's not optimal, because it does not leave enough room to assign the yellow process D.

OptaPlanner does find the more optimal solution fast, by using additional, smarter algorithms. And it scales too: both in data (more processes, more computers) and constraints (more hardware requirements, other constraints). So let's take a look how we can use Planner for this.

Try it yourself. Download and configure the examples in your favorite IDE. Run org.optaplanner.examples.cloudbalancing.app.CloudBalancingHelloWorld. By default, it is configured to run for 120 seconds. It will execute this code:


The code above does this:

  • Build the Solver based on a solver configuration (in this case an XML file from the classpath).

            SolverFactory solverFactory = SolverFactory.createFromXmlResource(
    
                    "org/optaplanner/examples/cloudbalancing/solver/cloudBalancingSolverConfig.xml");
            Solver solver = solverFactory.buildSolver();
  • Load the problem. CloudBalancingGenerator generates a random problem: you'll replace this with a class that loads a real problem, for example from a database.

            CloudBalance unsolvedCloudBalance = new CloudBalancingGenerator().createCloudBalance(400, 1200);
  • Solve the problem.

            solver.solve(unsolvedCloudBalance);
    
            CloudBalance solvedCloudBalance = (CloudBalance) solver.getBestSolution();
  • Display the result.

            System.out.println("\nSolved cloudBalance with 400 computers and 1200 processes:\n"
    
                    + toDisplayString(solvedCloudBalance));

The only non-obvious part is building the Solver. Let's examine that.

Take a look at the solver configuration:


This solver configuration consists out of 3 parts:

  • Domain model configuration: What can Planner change? We need to make Planner aware of our domain classes:

    
      <solutionClass>org.optaplanner.examples.cloudbalancing.domain.CloudBalance</solutionClass>
      <entityClass>org.optaplanner.examples.cloudbalancing.domain.CloudProcess</entityClass>
  • Score configuration: How should Planner optimize the planning variables? Since we have hard and soft constraints, we use a HardSoftScore. But we also need to tell Planner how to calculate such the score, depending on our business requirements. Further down, we 'll look into 2 alternatives to calculate the score: using a simple Java implementation or using Drools DRL.

    
      <scoreDirectorFactory>
        <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
        <easyScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingEasyScoreCalculator</easyScoreCalculatorClass>
        <!--<scoreDrl>org/optaplanner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
        <initializingScoreTrend>ONLY_DOWN</initializingScoreTrend>
      </scoreDirectorFactory>
  • Optimization algorithms configuration: How should Planner optimize it? Don't worry about this for now: this is a good default configuration that works on most planning problems. It will already surpass human planners and most in-house implementations. Using the Planner benchmark toolkit, you can tweak it to get even better results.

    
      <termination>
        <secondsSpentLimit>120</secondsSpentLimit>
      </termination>
      <constructionHeuristic>
        <constructionHeuristicType>FIRST_FIT_DECREASING</constructionHeuristicType>
      </constructionHeuristic>
      <localSearch>
        <acceptor>
          <entityTabuSize>7</entityTabuSize>
        </acceptor>
        <forager>
          <acceptedCountLimit>1000</acceptedCountLimit>
        </forager>
      </localSearch>

Let's examine the domain model classes and the score configuration.

The class CloudBalance implements the Solution interface. It holds a list of all computers and processes. We need to tell Planner how to retrieve the collection of process which it can change, so we need to annotate the getter getProcessList with @PlanningEntityCollectionProperty.

The CloudBalance class also has a property score which is the Score of that Solution instance in it's current state:


The method getProblemFacts() is only needed for score calculation with Drools. It's not needed for the other score calculation types.

Planner will search for the Solution with the highest Score. We're using a HardSoftScore, which means Planner will look for the solution with no hard constraints broken (fulfill hardware requirements) and as little as possible soft constraints broken (minimize maintenance cost).

Of course, Planner needs to be told about these domain-specific score constraints. There are several ways to implement such a score function:

Let's take a look at 2 different implementations:

One way to define a score function is to implement the interface EasyScoreCalculator in plain Java.


  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
    <easyScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingEasyScoreCalculator</easyScoreCalculatorClass>
  </scoreDirectorFactory>

Just implement the method calculateScore(Solution) to return a HardSoftScore instance.

Example 2.6. CloudBalancingEasyScoreCalculator.java

public class CloudBalancingEasyScoreCalculator implements EasyScoreCalculator<CloudBalance> {


    /**
     * A very simple implementation. The double loop can easily be removed by using Maps as shown in
     * {@link CloudBalancingMapBasedEasyScoreCalculator#calculateScore(CloudBalance)}.
     */
    public HardSoftScore calculateScore(CloudBalance cloudBalance) {
        int hardScore = 0;
        int softScore = 0;
        for (CloudComputer computer : cloudBalance.getComputerList()) {
            int cpuPowerUsage = 0;
            int memoryUsage = 0;
            int networkBandwidthUsage = 0;
            boolean used = false;
            // Calculate usage
            for (CloudProcess process : cloudBalance.getProcessList()) {
                if (computer.equals(process.getComputer())) {
                    cpuPowerUsage += process.getRequiredCpuPower();
                    memoryUsage += process.getRequiredMemory();
                    networkBandwidthUsage += process.getRequiredNetworkBandwidth();
                    used = true;
                }
            }
            
            // Hard constraints
            int cpuPowerAvailable = computer.getCpuPower() - cpuPowerUsage;
            if (cpuPowerAvailable < 0) {
                hardScore += cpuPowerAvailable;
            }
            int memoryAvailable = computer.getMemory() - memoryUsage;
            if (memoryAvailable < 0) {
                hardScore += memoryAvailable;
            }
            int networkBandwidthAvailable = computer.getNetworkBandwidth() - networkBandwidthUsage;
            if (networkBandwidthAvailable < 0) {
                hardScore += networkBandwidthAvailable;
            }
            
            // Soft constraints
            if (used) {
                softScore -= computer.getCost();
            }
        }
        return HardSoftScore.valueOf(hardScore, softScore);
    }
}

Even if we optimize the code above to use Maps to iterate through the processList only once, it is still slow because it doesn't do incremental score calculation. To fix that, either use an incremental Java score function or a Drools score function. Let's take a look at the latter.

To use the Drools rule engine as a score function, simply add a scoreDrl resource in the classpath:


  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
    <scoreDrl>org/optaplanner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>
  </scoreDirectorFactory>

First, we want to make sure that all computers have enough CPU, RAM and network bandwidth to support all their processes, so we make these hard constraints:


Next, if those constraints are met, we want to minimize the maintenance cost, so we add that as a soft constraint:


If you use the Drools rule engine for score calculation, you can integrate with other Drools technologies, such as decision tables (XLS or web based), the KIE Workbench rule repository, ...

OptaPlanner has several examples. In this manual we explain OptaPlanner mainly using the n queens example and cloud balancing example. So it's advisable to read at least those sections.

The source code of all these examples is available in the distribution zip under examples/sources and also in git under optaplanner/optaplanner-examples.

Table 3.1. Examples overview

SpeExampleDomainSizeCompetition?Special features used
N queens
  • 1 entity class

  • 1 variable

  • Entity <= 256

  • Value <= 256

  • Search space <= 10^616

None
Cloud balancing
  • 1 entity class

  • 1 variable

  • Entity <= 2400

  • Value <= 800

  • Search space <= 10^6967

  • No

  • Definied by us

Traveling salesman
  • 1 entity class

  • 1 chained variable

  • Entity <= 980

  • Value <= 980

  • Search space <= 10^2927

Dinner party
  • 1 entity class

  • 1 variable

  • Entity <= 144

  • Value <= 72

  • Search space <= 10^310

  • Unrealistic

  • Decision Table spreadsheet for score constraints

Tennis club scheduling
  • 1 entity class

  • 1 variable

  • Entity <= 72

  • Value <= 7

  • Search space <= 10^60

  • No

  • Definied by us

Course timetabling
  • 1 entity class

  • 2 variables

  • Entity <= 434

  • Value <= 25 and <= 20

  • Search space <= 10^1171

Machine reassignment
  • 1 entity class

  • 1 variable

  • Entity <= 50000

  • Value <= 5000

  • Search space <= 10^184948

Vehicle routing
  • 1 entity class

  • 1 chained variable

  • 1 shadow entity class

  • 1 automatic shadow variable

  • Entity <= 134

  • Value <= 141

  • Search space <= 10^285

Vehicle routing with time windows

Extra on Vehicle routing:

  • 1 shadow variable

  • Entity <= 1000

  • Value <= 1250

  • Search space <= 10^3000

Project job scheduling
  • 1 entity class

  • 2 variables

  • 1 shadow variable

  • Entity <= 640

  • Value <= ? and <= ?

  • Search space <= ?

Hospital bed planning
  • 1 entity class

  • 1 nullable variable

  • Entity <= 2750

  • Value <= 471

  • Search space <= 10^6851

  • Overconstrained planning

Exam timetabling
  • 2 entity classes (same hierarchy)

  • 2 variables

  • Entity <= 1096

  • Value <= 80 and <= 49

  • Search space <= 10^3374

Employee rostering
  • 1 entity class

  • 1 variable

  • Entity <= 752

  • Value <= 50

  • Search space <= 10^1277

Traveling tournament
  • 1 entity class

  • 1 variable

  • Entity <= 1560

  • Value <= 78

  • Search space <= 10^2951

None
Cheap time scheduling
  • 1 entity class

  • 2 variables

  • Entity <= 500

  • Value <= 100 and <= 288

  • Search space <= 10^20078

  • Nearly realistic

  • ICON

None

A realistic competition is an official, independent competition:

  • that clearly defines a real-word use case

  • with real-world constraints

  • with multiple, real-world datasets

  • that expects reproducible results within a specific time limit on specific hardware

  • that has had serious participation from the academic and/or enterprise Operations Research community

These realistic competitions provide an objective comparison of OptaPlanner with competitive software and academic research.

Use a good domain model: it will be easier to understand and solve your planning problem. This is the domain model for the n queens example:

public class Column {

    
    private int index;
    // ... getters and setters
}
public class Row {

    
    private int index;
    // ... getters and setters
}
public class Queen {

    
    private Column column;
    private Row row;
    public int getAscendingDiagonalIndex() {...}
    public int getDescendingDiagonalIndex() {...}
    // ... getters and setters
}

A Queen instance has a Column (for example: 0 is column A, 1 is column B, ...) and a Row (its row, for example: 0 is row 0, 1 is row 1, ...). Based on the column and the row, the ascending diagonal line as well as the descending diagonal line can be calculated. The column and row indexes start from the upper left corner of the chessboard.

public class NQueens implements Solution<SimpleScore> {

    
    private int n;
    private List<Column> columnList;
    private List<Row> rowList;
    private List<Queen> queenList;
    private SimpleScore score;
    // ... getters and setters
}

A single NQueens instance contains a list of all Queen instances. It is the Solution implementation which will be supplied to, solved by and retrieved from the Solver. Notice that in the 4 queens example, NQueens's getN() method will always return 4.


When 2 queens share the same column, row or diagonal line, such as (*) and (**), they can attack each other.

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.
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.

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.

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.

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.

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.

Hard constraints:

Medium constraints:

Soft constraints:

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

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.

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

Hard constraints:

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

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.

There are 3 dataset types:

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.
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.
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.

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.

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:

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.

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.

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.

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.

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;
    }

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;
    }

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).

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.

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();
    }
}

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.

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:

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:

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

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:

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.

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.

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.

A planning clone of a Solution must fulfill these requirements:

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

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;
        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.

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;
    }

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);
        }

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:

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

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>

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.

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.

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

An easy way to implement your score calculation in Java.

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);

A way to implement your score calculation incrementally in Java.

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>>(* 2);
        descendingDiagonalIndexMap = new HashMap<Integer, List<Queen>>(* 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 (!= 0) {
                ascendingDiagonalIndexMap.put(- 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();
    
}

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

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

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>

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.

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.

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:

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.

There are several ways to deal with a score trap:

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.

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

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:

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.

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

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.

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:

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

Next, add Late Acceptance behind it:

  1. First Fit Decreasing

  2. 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:

  1. First Fit Decreasing

  2. 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:

  1. First Fit Decreasing

  2. Late Acceptance (relatively long time)

  3. Tabu Search (relatively short time)

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.

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.

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.

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.

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.

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.

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:

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.

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.

Simplest configuration:


    <pillarChangeMoveSelector/>

Advanced configuration:


    <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>

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.

The other properties are explained in changeMoveSelector.

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.

Simplest configuration:


    <subChainChangeMoveSelector/>

Advanced configuration:


    <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>

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

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

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

Simplest configuration:


    <unionMoveSelector>
      <...MoveSelector/>
      <...MoveSelector/>
      <...MoveSelector/>
      ...
    </unionMoveSelector>

Advanced configuration:


    <unionMoveSelector>
      ... <!-- Normal selector properties -->
      <selectorProbabilityWeightFactoryClass>...ProbabilityWeightFactory</selectorProbabilityWeightFactoryClass>
      <changeMoveSelector>
        <fixedProbabilityWeight>...</fixedProbabilityWeight>
        ...
      </changeMoveSelector>
      <swapMoveSelector>
        <fixedProbabilityWeight>...</fixedProbabilityWeight>
        ...
      </swapMoveSelector>
      <...MoveSelector>
        <fixedProbabilityWeight>...</fixedProbabilityWeight>
        ...
      </...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.

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>

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:

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.

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:

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.

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

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.

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.

Some Selector types implement a SorterManner out of the box:

  • EntitySelector supports:

  • 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>

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>

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.

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.

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.

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

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 (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.

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

public interface MoveListFactory<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>

Advanced configuration:


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

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:

Exhaustive Search variants suffer from 2 big scalability issues:

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.

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:

  1. Put all entities in a queue.

  2. Assign the first entity (from that queue) to the best value.

  3. Repeat until all entities are assigned.

Simple configuration:


  <constructionHeuristic>
    <constructionHeuristicType>ALLOCATE_ENTITY_FROM_QUEUE</constructionHeuristicType>
  </constructionHeuristic>

Verbose simple configuration:


  <constructionHeuristic>
    <constructionHeuristicType>ALLOCATE_ENTITY_FROM_QUEUE</constructionHeuristicType>
    <entitySorterManner>DECREASING_DIFFICULTY_IF_AVAILABLE</entitySorterManner>
    <valueSorterManner>INCREASING_STRENGTH_IF_AVAILABLE</valueSorterManner>
  </constructionHeuristic>

The entitySorterManner options are:

The valueSorterManner options are:

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.

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

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>

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>

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

  1. Put all entity-value combinations in a pool.

  2. Assign the best entity to best value.

  3. Repeat until all entities are assigned.

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:


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:


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:

  1. B0 to B3

  2. D0 to B2

  3. 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.

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

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.

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.

When Tabu Search takes steps it creates one or more tabu's. For a number of steps, it does not accept a move if that move breaks tabu. That number of steps is the tabu size.


  <localSearch>
    ...
    <acceptor>
      <entityTabuSize>7</entityTabuSize>
    </acceptor>
    <forager>
      <acceptedCountLimit>1000</acceptedCountLimit>
    </forager>
  </localSearch>

OptaPlanner implements several tabu types:

You can even combine tabu types:


    <acceptor>
      <entityTabuSize>7</entityTabuSize>
      <valueTabuSize>3</valueTabuSize>
    </acceptor>

If you pick a too small tabu size, your solver can still get stuck in a local optimum. On the other hand, with the exception of solution tabu, if you pick a too large tabu size, your solver can get stuck by bouncing of the walls. Use the Benchmarker to fine tweak your configuration.

Simulated Annealing does not always pick the move with the highest score, neither does it evaluate many moves per step. At least at first. Instead, it gives non improving moves also a chance to be picked, depending on its score and the time gradient of the Termination. In the end, it gradually turns into Hill Climbing, only accepting improving moves.

Start with a simulatedAnnealingStartingTemperature set to the maximum score delta a single move can cause. Use the Benchmarker to tweak the value.


  <localSearch>
    ...
    <acceptor>
      <simulatedAnnealingStartingTemperature>2hard/100soft</simulatedAnnealingStartingTemperature>
    </acceptor>
    <forager>
      <acceptedCountLimit>1</acceptedCountLimit>
    </forager>
  </localSearch>

Simulated Annealing should use a low acceptedCountLimit. The classic algorithm uses an acceptedCountLimit of 1, but often 4 performs better.

You can even combine it with a tabu acceptor at the same time. That gives Simulated Annealing salted with a bit of Tabu. Use a lower tabu size than in a pure Tabu Search configuration.


  <localSearch>
    ...
    <acceptor>
      <simulatedAnnealingStartingTemperature>2hard/100soft</simulatedAnnealingStartingTemperature>
      <entityTabuSize>5</entityTabuSize>
    </acceptor>
    <forager>
      <acceptedCountLimit>1</acceptedCountLimit>
    </forager>
  </localSearch>
14.1. Finding the best Solver configuration
14.2. Doing a benchmark
14.2.1. Adding a dependency on optaplanner-benchmark
14.2.2. Building and running a PlannerBenchmark
14.2.3. SolutionFileIO: input and output of Solution files
14.2.4. Warming up the HotSpot compiler
14.2.5. Benchmark blueprint: a predefined configuration
14.2.6. Writing the output solution of the benchmark runs
14.2.7. Benchmark logging
14.3. Benchmark report
14.3.1. HTML report
14.3.2. Ranking the Solvers
14.4. Summary statistics
14.4.1. Best score summary (graph and table)
14.4.2. Best score scalability summary (graph)
14.4.3. Winning score difference summary (graph and table)
14.4.4. Worst score difference percentage (ROI) summary (graph and table)
14.4.5. Average calculation count summary (graph and table)
14.4.6. Time spent summary (graph and table)
14.4.7. Time spent scalability summary (graph)
14.4.8. Best score per time spent summary (graph)
14.5. Statistic per dataset (graph and CSV)
14.5.1. Enabling a problem statistic
14.5.2. Best score over time statistic (graph and CSV)
14.5.3. Step score over time statistic (graph and CSV)
14.5.4. Calculate count per second statistic (graph and CSV)
14.5.5. Best solution mutation over time statistic (graph and CSV)
14.5.6. Move count per step statistic (graph and CSV)
14.5.7. Memory use statistic (graph and CSV)
14.6. Statistic per single benchmark (graph and CSV)
14.6.1. Enabling a single statistic
14.6.2. Constraint match total best score over time statistic (graph and CSV)
14.6.3. Constraint match total step score over time statistic (graph and CSV)
14.6.4. Picked move type best score diff over time statistic (graph and CSV)
14.6.5. Picked move type step score diff over time statistic (graph and CSV)
14.7. Advanced benchmarking
14.7.1. Benchmarking performance tricks
14.7.2. Template based benchmarking and matrix benchmarking
14.7.3. Benchmark report aggregation

Build a PlannerBenchmark instance with a PlannerBenchmarkFactory. Configure it with a benchmark configuration XML file, provided as a classpath resource:

        PlannerBenchmarkFactory plannerBenchmarkFactory = PlannerBenchmarkFactory.createFromXmlResource(

                "org/optaplanner/examples/nqueens/benchmark/nqueensBenchmarkConfig.xml");
        PlannerBenchmark plannerBenchmark = benchmarkFactory.buildPlannerBenchmark();
        plannerBenchmark.benchmark();

A benchmark configuration file looks like this:


<?xml version="1.0" encoding="UTF-8"?>
<plannerBenchmark>
  <benchmarkDirectory>local/data/nqueens</benchmarkDirectory>

  <inheritedSolverBenchmark>
    <problemBenchmarks>
      ...
      <inputSolutionFile>data/nqueens/unsolved/32queens.xml</inputSolutionFile>
      <inputSolutionFile>data/nqueens/unsolved/64queens.xml</inputSolutionFile>
    </problemBenchmarks>
    <solver>
      ...<!-- Common solver configuration -->
    </solver>
  </inheritedSolverBenchmark>

  <solverBenchmark>
    <name>Tabu Search</name>
    <solver>
      ...<!-- Tabu Search specific solver configuration -->
    </solver>
  </solverBenchmark>
  <solverBenchmark>
    <name>Simulated Annealing</name>
    <solver>
      ...<!-- Simulated Annealing specific solver configuration -->
    </solver>
  </solverBenchmark>
  <solverBenchmark>
    <name>Late Acceptance</name>
    <solver>
      ...<!-- Late Acceptance specific solver configuration -->
    </solver>
  </solverBenchmark>
</plannerBenchmark>

This PlannerBenchmark will try 3 configurations (Tabu Search, Simulated Annealing and Late Acceptance) on 2 data sets (32queens and 64queens), so it will run 6 solvers.

Every <solverBenchmark> element contains a solver configuration and one or more <inputSolutionFile> elements. It will run the solver configuration on each of those unsolved solution files. The element name is optional, because it is generated if absent. The inputSolutionFile is read by a SolutionFileIO (relative to the working directory).

Note

Use a forward slash (/) as the file separator (for example in the element <inputSolutionFile>). That will work on any platform (including Windows).

Do not use backslash (\) as the file separator: that breaks portability because it does not work on Linux and Mac.

To lower verbosity, the common parts of multiple <solverBenchmark> elements are extracted to the <inheritedSolverBenchmark> element. Every property can still be overwritten per <solverBenchmark> element. Note that inherited solver phases such as <constructionHeuristic> or <localSearch> are not overwritten but instead are added to the tail of the solver phases list.

The benchmark report will be written in the directory specified the <benchmarkDirectory> element (relative to the working directory).

Note

It's recommended that the benchmarkDirectory is a directory ignored for source control and not cleaned by your build system. This way the generated files are not bloating your source control and they aren't lost when doing a build. Usually that directory is called local.

If an Exception or Error occurs in a single benchmark, the entire Benchmarker will not fail-fast (unlike everything else in OptaPlanner). Instead, the Benchmarker will continue to run all other benchmarks, write the benchmark report and then fail (if there is at least 1 failing single benchmark). The failing benchmarks will be clearly marked as such in the benchmark report.