OptaPlanner User Guide

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

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

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

Usually, a planning problem has at least 2 levels of constraints:

Some problems have positive constraints too:

Some basic problems (such as N Queens) only have hard constraints. Some problems have 3 or more levels of constraints, for example hard, medium and soft constraints.

These constraints define the score calculation (AKA fitness function) of a planning problem. Each solution of a planning problem can be graded with a score. With OptaPlanner, score constraints are written in an Object Oriented language, such as Java code or Drools rules. Such code is easy, flexible and scalable.

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. Pick an example to try it out:

Besides the GUI examples, there are also a set of webexamples to try out:

Pick an example to try it out, such as the Vehicle Routing example:

To run the examples in your favorite IDE:

To run a specific example directly and skip the example selection window, run its App class (for example CloudBalancingApp) instead of OptaPlannerExamplesApp.

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 in your classpath.

It's easy to build OptaPlanner from source:

OptaPlanner is:

OptaPlanner separates its API and implementation:

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

Public questions are welcome on our community forum. Bugs and feature requests are welcome in our issue tracker. Pull requests are very welcome on GitHub and get priority treatment! By open sourcing your improvements, you 'll benefit from our peer review and from our improvements made on top of your improvements.

Red Hat sponsors OptaPlanner development by employing the core team. For enterprise support and consulting, take a look at the BRMS and BPMS products (which contain OptaPlanner) or contact Red Hat.

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.

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 four constraints.

The following hard constraints must be fulfilled:

The following soft constraints should be optimized:

This problem is a form of bin packing. The following is a simplified example, where we assign four processes to two computers with two 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 is not optimal, as it does not leave enough room to assign the yellow process "D".

Planner does find the more optimal solution fast by using additional, smarter algorithms. It also scales: both in data (more processes, more computers) and constraints (more hardware requirements, other constraints). So see how Planner can be used in this scenario.

Beginning with the domain model:

In the UML class diagram above, the Planner concepts are already annotated:

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

public class CloudBalancingHelloWorld {

    public static void main(String[] args) {
        // Build the Solver
        SolverFactory<CloudBalance> solverFactory = SolverFactory.createFromXmlResource(
                "org/optaplanner/examples/cloudbalancing/solver/cloudBalancingSolverConfig.xml");
        Solver<CloudBalance> solver = solverFactory.buildSolver();

        // Load a problem with 400 computers and 1200 processes
        CloudBalance unsolvedCloudBalance = new CloudBalancingGenerator().createCloudBalance(400, 1200);

        // Solve the problem
        CloudBalance solvedCloudBalance = solver.solve(unsolvedCloudBalance);

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

    ...
}

The only complicated part is building the Solver, as detailed in the next section.

Take a look at the solver configuration:

<?xml version="1.0" encoding="UTF-8"?>
<solver>
  <!-- Domain model configuration -->
  <scanAnnotatedClasses/>

  <!-- Score configuration -->
  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
    <easyScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingEasyScoreCalculator</easyScoreCalculatorClass>
    <!--<scoreDrl>org/optaplanner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
  </scoreDirectorFactory>

  <!-- Optimization algorithms configuration -->
  <termination>
    <secondsSpentLimit>30</secondsSpentLimit>
  </termination>
</solver>

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

Planner will search for the Solution with the highest Score. This example uses 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 two different implementations:

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

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);
    }

}

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

...

import org.optaplanner.examples.cloudbalancing.domain.CloudBalance;
import org.optaplanner.examples.cloudbalancing.domain.CloudComputer;
import org.optaplanner.examples.cloudbalancing.domain.CloudProcess;

global HardSoftScoreHolder scoreHolder;

// ############################################################################
// Hard constraints
// ############################################################################

rule "requiredCpuPowerTotal"
    when
        $computer : CloudComputer($cpuPower : cpuPower)
        $requiredCpuPowerTotal : Number(intValue > $cpuPower) from accumulate(
            CloudProcess(
                computer == $computer,
                $requiredCpuPower : requiredCpuPower),
            sum($requiredCpuPower)
        )
    then
        scoreHolder.addHardConstraintMatch(kcontext, $cpuPower - $requiredCpuPowerTotal.intValue());
end

rule "requiredMemoryTotal"
    ...
end

rule "requiredNetworkBandwidthTotal"
    ...
end

// ############################################################################
// Soft constraints
// ############################################################################

rule "computerCost"
    when
        $computer : CloudComputer($cost : cost)
        exists CloudProcess(computer == $computer)
    then
        scoreHolder.addSoftConstraintMatch(kcontext, - $cost);
end

Now that this simple example works, try going further. Enrich the domain model and add extra constraints such as these:

This example is explained in a tutorial.

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

       SolverFactory<NQueens> solverFactory = SolverFactory.createFromXmlResource(
               "org/optaplanner/examples/nqueens/solver/nqueensSolverConfig.xml");
       Solver<NQueens> 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. 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.

       SolverFactory<NQueens> solverFactory = SolverFactory.createFromXmlResource(
               ".../nqueensSolverConfig.xml", getClass().getClassLoader());

       KieServices kieServices = KieServices.Factory.get();
       KieContainer kieContainer = kieServices.newKieContainer(
               kieServices.newReleaseId("org.nqueens", "nqueens", "1.0.0"));
       SolverFactory<NQueens> solverFactory = SolverFactory.createFromKieContainerXmlResource(
               kieContainer, ".../nqueensSolverConfig.xml");

Both a Solver and a SolverFactory have a generic type called Solution_, which is the class representing a planning problem and solution.

A solver configuration XML 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 algorithms (optional) -->
  <termination>
    ...
  </termination>
  <constructionHeuristic>
    ...
  </constructionHeuristic>
  <localSearch>
    ...
  </localSearch>
</solver>

Notice the three parts in it:

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

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

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

        SolverFactory<NQueens> solverFactory = SolverFactory.createFromXmlResource(
                "org/optaplanner/examples/nqueens/solver/nqueensSolverConfig.xml");

        TerminationConfig terminationConfig = new TerminationConfig();
        terminationConfig.setMinutesSpentLimit(userInput);
        solverFactory.getSolverConfig().setTerminationConfig(terminationConfig);

        Solver<NQueens> solver = solverFactory.buildSolver();

Every element in the solver configuration XML is available as a *Config class or a property on a *Config class in the package namespace org.optaplanner.core.config. These *Config classes are the Java representation of the XML format. They build the runtime components (of the package namespace org.optaplanner.core.impl) and assemble them into an efficient Solver.

    private SolverFactory<NQueens> base;

    public void init() {
        base = SolverFactory.createFromXmlResource(
                "org/optaplanner/examples/nqueens/solver/nqueensSolverConfig.xml");
        base.getSolverConfig().setTerminationConfig(new TerminationConfig());
    }

    // Called concurrently from different threads
    public void userRequest(..., long userInput)
        SolverFactory<NQueens> solverFactory = base.cloneSolverFactory();
        solverFactory.getSolverConfig().getTerminationConfig().setMinutesSpentLimit(userInput);
        Solver<NQueens> solver = solverFactory.buildSolver();
        ...
    }

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

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 one planning entity class. Most use cases also have only one planning variable per planning entity class.

A good model can greatly improve the success of your planning implementation. Follow these guidelines to design a good model:

For inspiration, take a look at typical design patterns or how the examples modeled their domain:

In Planner, all problems facts and planning entities are plain old JavaBeans (POJOs). Load them from a database, an XML file, a data repository, a REST service, a noSQL cloud, ... (see integration): it doesn't matter.

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 Solver implementation will solve your planning problem.

public interface Solver<S extends Solution> {
    
    S solve(S planningProblem);

    ...
}

A Solver can only solve one planning problem instance at a time. A Solver should only be accessed from a single thread, except for the methods that are specifically javadocced as being thread-safe. It is built with a SolverFactory, there is no need to implement it yourself.

Solving a problem is quite easy once you have:

Just provide the planning problem as argument to the solve() method and it will return the best solution found:

    NQueens bestSolution = solver.solve(planningProblem);

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

Figure 4.2. Best Solution for the 4 Queens Puzzle in 8ms (Also an Optimal Solution)

The solve(Solution) method can take a long time (depending on the problem size and the solver configuration). The Solver intelligently wades through the search space of possible solutions and remembers the best solution it encounters during solving. Depending on a number factors (including problem size, how much time the Solver has, the solver configuration, ...), that best solution might or might not be an optimal solution.

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.

These are the 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 (Queen-2 {null -> Row-0}).
DEBUG     CH step (1), time spent (7), score (0), selected move count (3), picked move (Queen-1 {null -> Row-2}).
DEBUG     CH step (2), time spent (10), score (0), selected move count (4), picked move (Queen-3 {null -> Row-3}).
DEBUG     CH step (3), time spent (12), score (-1), selected move count (4), picked move (Queen-0 {null -> Row-1}).
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 (Queen-1 {Row-2 -> Row-3}).
DEBUG     LS step (1), time spent (24), score (0), new best score (0), accepted/selected move count (9/12), picked move (Queen-3 {Row-3 -> Row-2}).
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 are 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 org.optaplanner package in your logback.xml file:

<configuration>

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

  ...

<configuration>

If instead, you are still using Log4J 1.x (and you do not 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>

        MDC.put("tenant.name",tenantName);
        Solution bestSolution = solver.solve(planningProblem);
        MDC.remove("tenant.name");

  <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>

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

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

<solver>
  <randomSeed>0</randomSeed>
  ...
</solver>

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

<solver>
  <randomType>MERSENNE_TWISTER</randomType>
  ...
</solver>

The following types are supported:

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

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

Planner cannot automatically know which Solution is best for your business, so you need to tell it how to calculate the score of a given Solution according to your business needs. If you forget or are unable to implement an important business constraint, the solution is probably useless:

Defining constraints in Planner is very flexible through the following score techniques:

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

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

Most planning problems have only negative constraints and therefore have a negative score. In that case, the score is the sum of the weight of the negative constraints being broken, with a perfect score of 0. This explains why the score of a solution of four queens is the negative of the number of queen pairs which can attack each other.

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

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

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

Score weighting is often used in use cases where you can put a price tag on everything. In that case, the positive constraints maximize revenue and the negative constraints minimize expenses, so together they maximize profit. Alternatively, score weighting is also often used to create social fairness. For example, a nurse, who requests a free day, pays a higher weight on New Years eve than on a normal day.

Putting a good weight on a constraint can be a difficult analytical decision, because it is about making choices and tradeoffs with other constraints. However, a non-accurate weight is less damaging than mediocre algorithms:

The weight of a constraint match can be dynamically based on the planning entities involved. For example in cloud balance, the weight of the soft constraint match for an active Computer is the cost of that Computer (which differs per computer).

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 two score levels, hard and soft. Two scores 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.

If there are two (or more) score levels, for example a hard and soft level, then a score is feasible if no hard constraints are broken.

For each constraint, you need to pick a score level, a score weight and a score signum. For example: -1soft which has score level of soft, a weight of 1 and a negative signum. Do not use a big constraint weight when your business actually wants different score levels. That hack, known as score folding, is broken:

Three or more score levels are 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 two 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 is impossible to compare apples and oranges, we can not weight them against each other. Yet, despite that we can not compare them, we can state that two apples are better then one apple. Similarly, we can state that two apples and one orange are better than just one 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 is 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 (three apples and one orange) and solution B (one apple and six oranges). It is guaranteed that Planner has not found another solution which has more apples or more oranges or even a better combination of both (such as two apples and three 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.

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

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

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

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

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

  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
    ...
  </scoreDirectorFactory>

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

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

Additionally, floating point number addition is not associative:

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

This leads to score corruption.

Decimal numbers (BigDecimal) have none of these problems.

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

  <scoreDirectorFactory>
    <scoreDefinitionType>SIMPLE</scoreDefinitionType>
    ...
  </scoreDirectorFactory>

Variants of this scoreDefinitionType:

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

  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
    ...
  </scoreDirectorFactory>

Variants of this scoreDefinitionType:

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

  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_MEDIUM_SOFT</scoreDefinitionType>
    ...
  </scoreDirectorFactory>

Variants of this scoreDefinitionType:

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

  <scoreDirectorFactory>
    <scoreDefinitionType>BENDABLE</scoreDefinitionType>
    <bendableHardLevelsSize>2</bendableHardLevelsSize>
    <bendableSoftLevelsSize>3</bendableSoftLevelsSize>
    ...
  </scoreDirectorFactory>

The number of hard and soft score levels needs to be set at configuration time. It is not flexible to change during solving.

Variants of this scoreDefinitionType:

The ScoreDefinition interface defines the score representation.

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

Then hook your custom ScoreDefinition in your SolverConfig.xml:

  <scoreDirectorFactory>
    <scoreDefinitionClass>...MyScoreDefinition</scoreDefinitionClass>
    ...
  </scoreDirectorFactory>

To have it integrate seamlessly with JPA/Hibernate, XStream, ... you might need to write some glue code.

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

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

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

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>>(n * 2);
        descendingDiagonalIndexMap = new HashMap<Integer, List<Queen>>(n * 2);
        for (int i = 0; i < n; i++) {
            rowIndexMap.put(i, new ArrayList<Queen>(n));
            ascendingDiagonalIndexMap.put(i, new ArrayList<Queen>(n));
            descendingDiagonalIndexMap.put(i, new ArrayList<Queen>(n));
            if (i != 0) {
                ascendingDiagonalIndexMap.put(n - 1 + i, new ArrayList<Queen>(n));
                descendingDiagonalIndexMap.put((-i), new ArrayList<Queen>(n));
            }
        }
        score = 0;
        for (Queen queen : nQueens.getQueenList()) {
            insert(queen);
        }
    }

    public void beforeEntityAdded(Object entity) {
        // Do nothing
    }

    public void afterEntityAdded(Object entity) {
        insert((Queen) entity);
    }

    public void beforeVariableChanged(Object entity, String variableName) {
        retract((Queen) entity);
    }

    public void afterVariableChanged(Object entity, String variableName) {
        insert((Queen) entity);
    }

    public void beforeEntityRemoved(Object entity) {
        retract((Queen) entity);
    }

    public void afterEntityRemoved(Object entity) {
        // Do nothing
    }

    private void insert(Queen queen) {
        Row row = queen.getRow();
        if (row != null) {
            int rowIndex = queen.getRowIndex();
            List<Queen> rowIndexList = rowIndexMap.get(rowIndex);
            score -= rowIndexList.size();
            rowIndexList.add(queen);
            List<Queen> ascendingDiagonalIndexList = ascendingDiagonalIndexMap.get(queen.getAscendingDiagonalIndex());
            score -= ascendingDiagonalIndexList.size();
            ascendingDiagonalIndexList.add(queen);
            List<Queen> descendingDiagonalIndexList = descendingDiagonalIndexMap.get(queen.getDescendingDiagonalIndex());
            score -= descendingDiagonalIndexList.size();
            descendingDiagonalIndexList.add(queen);
        }
    }

    private void retract(Queen queen) {
        Row row = queen.getRow();
        if (row != null) {
            List<Queen> rowIndexList = rowIndexMap.get(queen.getRowIndex());
            rowIndexList.remove(queen);
            score += rowIndexList.size();
            List<Queen> ascendingDiagonalIndexList = ascendingDiagonalIndexMap.get(queen.getAscendingDiagonalIndex());
            ascendingDiagonalIndexList.remove(queen);
            score += ascendingDiagonalIndexList.size();
            List<Queen> descendingDiagonalIndexList = descendingDiagonalIndexMap.get(queen.getDescendingDiagonalIndex());
            descendingDiagonalIndexList.remove(queen);
            score += descendingDiagonalIndexList.size();
        }
    }

    public SimpleScore calculateScore() {
        return SimpleScore.valueOf(score);
    }

}

Configure it in your solver configuration:

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

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

public interface ConstraintMatchAwareIncrementalScoreCalculator<Sol extends Solution> {

    void resetWorkingSolution(Sol workingSolution, boolean constraintMatchEnabled);

    Collection<ConstraintMatchTotal> getConstraintMatchTotals();
    
}

The InitializingScoreTrend specifies how the Score will change as more and more variables are initialized (while the already initialized variables do not 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:

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.

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

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

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

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

Figure 5.1. Incremental Score Calculation for the 4 Queens Puzzle

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

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

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

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

This can give a good performance gain in some use cases, not just because the score calculation is faster, but mainly because most optimization algorithms will spend less time evaluating unfeasible solutions. However, usually this not a good idea because 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 one score constraint can kill the score calculation performance outright. Use the Benchmarker to do a one minute run and check what happens to the average calculation count per second if you comment out all but one of the score constraints.

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

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

As shown above, the squared workload implementation guarantees that if you select two employees from a given solution and make their distribution between those two employees fairer, then the resulting new solution will have a better overall score. Don not just use the difference from the average workload, as that can lead to unfairness, as demonstrated below.

When the workload is perfect balanced, the user often likes to see a 0 score, instead of the distracting -34soft in the image above (for the last solution which is almost perfectly balanced). To nullify this, either add the average multiplied by the number of entities to the score or instead show the variance or standard deviation in the UI.

Terminates when an amount of time has been used.

  <termination>
    <millisecondsSpentLimit>500</millisecondsSpentLimit>
  </termination>

  <termination>
    <secondsSpentLimit>10</secondsSpentLimit>
  </termination>

  <termination>
    <minutesSpentLimit>5</minutesSpentLimit>
  </termination>

  <termination>
    <hoursSpentLimit>1</hoursSpentLimit>
  </termination>

  <termination>
    <daysSpentLimit>2</daysSpentLimit>
  </termination>

Multiple time types can be used together, for example to configure 150 minutes, either configure it directly:

  <termination>
    <minutesSpentLimit>150</minutesSpentLimit>
  </termination>

Or use a combination that sums up to 150 minutes:

  <termination>
    <hoursSpentLimit>2</hoursSpentLimit>
    <minutesSpentLimit>30</minutesSpentLimit>
  </termination>

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

  <localSearch>
    <termination>
      <unimprovedMillisecondsSpentLimit>500</unimprovedMillisecondsSpentLimit>
    </termination>
  </localSearch>

  <localSearch>
    <termination>
      <unimprovedSecondsSpentLimit>10</unimprovedSecondsSpentLimit>
    </termination>
  </localSearch>

  <localSearch>
    <termination>
      <unimprovedMinutesSpentLimit>5</unimprovedMinutesSpentLimit>
    </termination>
  </localSearch>

  <localSearch>
    <termination>
      <unimprovedHoursSpentLimit>1</unimprovedHoursSpentLimit>
    </termination>
  </localSearch>