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Chapter 4. Planner Configuration

4.1. Overview
4.2. Solver Configuration
4.2.1. Solver Configuration by XML
4.2.2. Solver Configuration by Java API
4.2.3. Annotations Configuration
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 Range
4.3.6. Shadow Variable
4.3.7. Planning Problem and Planning Solution
4.4. Use the Solver
4.4.1. The Solver Interface
4.4.2. Solving a Problem
4.4.3. Environment Mode: Are There Bugs in my Code?
4.4.4. Logging Level: What is the Solver Doing?
4.4.5. Random Number Generator

4.1. Overview

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

  1. Model your planning problem as a class that implements the interface Solution, for example the class NQueens.

  2. Configure a Solver, for example a First Fit and Tabu Search solver for any NQueens instance.

  3. Load a problem data set from your data layer, for example a 4 Queens instance. That is the planning problem.

  4. Solve it with Solver.solve(planningProblem) which retuns the best solution found.

4.2. Solver Configuration

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.

Note

On some environments (OSGi, JBoss modules, ...), classpath resources (such as the solver config, score DRL's and domain classes) in your jars might not be available to the default ClassLoader of the optaplanner-core jar. In those cases, provide the ClassLoader of your classes as a parameter:

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

Note

When using Workbench or Execution Server or to take advantage of Drools's KieContainer features, provide the KieContainer as a parameter:

       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");

And use a ksessionName in the solver configuration.

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:

  • Define the model.

  • Define the score function.

  • Optionally configure the optimization algorithm(s).

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.

Important

The SolverFactory is only multi-thread safe after its configured. So the getSolverConfig() method is not thread-safe. To configure a SolverFactory dynamically for each user request, build a SolverFactory as base during initialization and clone it with the cloneSolverFactory() method for a user request:

    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();
        ...
    }

Instead of the declaring the classes that have a @PlanningSolution or @PlanningEntity manually:

<solver>
  <!-- Define the model -->
  <solutionClass>org.optaplanner.examples.nqueens.domain.NQueens</solutionClass>
  <entityClass>org.optaplanner.examples.nqueens.domain.Queen</entityClass>

  ...
</solver>

Planner can find scan the classpath and find them automatically:

<solver>
  <!-- Define the model -->
  <scanAnnotatedClasses/>

  ...
</solver>

If there are multiple models in your classpath (or just to speed up scanning), specify the packages to scan:

<solver>
  <!-- Define the model -->
  <scanAnnotatedClasses>
    <packageInclude>org.optaplanner.examples.cloudbalancing</packageInclude>
  </scanAnnotatedClasses>

  ...
</solver>

This will find all solution and entity classes in the package or subpackages.

Note

If scanAnnotatedClasses is not specified, the org.reflections transitive maven dependency can be excluded.

Planner needs to be told which classes in your domain model are planning entities, which properties are planning variables, etc. There are several ways to deliver this information:

This manual focuses on the first manner, but every features supports all 3 manners, even if it's not explicitly mentioned.

4.3. Model a Planning Problem

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.

Note

In real-time planning, even though the problem itself changes, problem facts do not really change during planning, instead they change between planning (because the Solver temporarily stops to apply the problem fact changes).

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

  • In a many to one relationship, it is normally the many side that is the planning entity class. The property referencing the other side is then the planning variable. For example in employee rostering: the planning entity class is ShiftAssignment, not Employee, and the planning variable is ShiftAssignment.getEmployee() because one Employee has multiple ShiftAssignments but one ShiftAssignment has only one Employee.

  • A planning entity class should have at least one problem property. A planning entity class with only planning variables can normally be simplified by converting one of those planning variables into a problem property. That heavily decreases the search space size. For example in employee rostering: the ShiftAssignment's getShift() is a problem property and the getEmployee() is a planning variable. If both were a planning variable, solving it would be far less efficient.

    • A surrogate ID does not suffice as the required minimum of one problem property. It needs to be understandable by the business. A business key does suffice. This prevents an unassigned entity from being nameless (unidentifiable by the business).

    • This way, there is no need to add a hard constraint to assure that two planning entities are different: they are already different due to their problem properties.

    • In some cases, multiple planning entities have the same problem property. In such cases, it can be useful to create an extra problem property to distinguish them. For example in employee rostering: ShiftAssignment has besides the problem property Shift also the problem property indexInShift.

  • The number of planning entities is recommended to be fixed during planning. When unsure of which property should be a planning variable and which should be a problem property, choose it so the number of planning entities is fixed. For example in employee rostering: if the planning entity class would have been EmployeeAssignment with a problem property getEmployee() and a planning variable getShift(), than it is impossible to accurately predict how many EmployeeAssignment instances to make per Employee.

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

Note

Vehicle routing is special, because it uses a chained planning variable.

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.

Note

Generally, better designed domain classes lead to simpler and more efficient score constraints. Therefore, when dealing with a messy (denormalized) legacy system, it can sometimes be worthwhile to convert the messy domain model into a Planner specific model first. For example: if your domain model has two Teacher instances for the same teacher that teaches at two different departments, it is harder to write a correct score constraint that constrains a teacher's spare time on the original model than on an adjusted model.

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

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 is usually only one 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 one or more planning variables. It should also have one 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 one course has multiple lectures). Each Lecture needs to be scheduled into a Period and a Room so it has two planning variables (period and room). For example: the course Mathematics has eight 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;

    // ...
}

Without automated scanning, the solver configuration also needs to declare each planning entity class:

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

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

Note

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

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

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

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 that 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.

Although most algorithms start with the more difficult entities first, they just reverse the ordering.

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

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

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

@PlanningEntity
public class Queen {
    ...

    private Row row;

    @PlanningVariable(valueRangeProviderRefs = {"rowRange"})
    public Row getRow() {
        return row;
    }

    public void setRow(Row row) {
        this.row = row;
    }

}

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

Note

A @PlanningVariable annotation needs to be on a member in a class with a @PlanningEntity annotation. It is ignored on parent classes or subclasses without that annotation.

Annotating the field instead of the property works too:

@PlanningEntity
public class Queen {
    ...

    @PlanningVariable(valueRangeProviderRefs = {"rowRange"})
    private Row row;

}

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 counterproductive. For example: in task assignment with too many tasks for the workforce, we would rather leave low priority tasks unassigned instead of assigning them to an overloaded worker.

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

    @PlanningVariable(..., nullable = true)
    public Worker getWorker() {
        return worker;
    }

Important

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

Note

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

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

    @PlanningVariable(..., nullable = true, reinitializeVariableEntityFilter = ReinitializeTaskFilter.class)
    public Worker getWorker() {
        return worker;
    }

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

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

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

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

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

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

This annotation can be located on 2 types of methods:

Note

A @ValueRangeProvider annotation needs to be on a member in a class with a @PlanningSolution or a @PlanningEntity annotation. It is ignored on parent classes or subclasses without those annotations.

The return type of that method can be 2 types:

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

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

    @PlanningVariable(valueRangeProviderRefs = {"rowRange"})
    public Row getRow() {
        return row;
    }
@PlanningSolution
public class NQueens implements Solution<SimpleScore> {

    // ...

    @ValueRangeProvider(id = "rowRange")
    public List<Row> getRowList() {
        return rowList;
    }

}

Important

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

Annotating the field instead of the property works too:

@PlanningSolution
public class NQueens implements Solution<SimpleScore> {
    ...

    @ValueRangeProvider(id = "rowRange")
    private List<Row> rowList;

}

Each planning entity has its own value range (a set of possible planning values) for the 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 = {"departmentRoomRange"})
    public Room getRoom() {
        return room;
    }

    @ValueRangeProvider(id = "departmentRoomRange")
    public List<Room> getPossibleRoomList() {
        return getCourse().getTeacher().getDepartment().getRoomList();
    }

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

Note

By limiting the value range specifically of one planning entity, you are effectively creating a built-in hard constraint. This can have the benefit of severely lowering the number of possible solutions; however, it can also away the freedom of the optimization algorithms to temporarily break that constraint in order to escape from a local optimum.

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

Every entity has its own List instance, unless multiple entities have the same value range. For example, if teacher A and B belong to the same department, they use the same List<Room> instance. Furthermore, each List contains a subset of the same set of planning value instances. For example, if department A and B can both use room X, then their List<Room> instances contain the same Room instance.

Note

A ValueRangeProvider on the planning entity consumes more memory than ValueRangeProvider on the Solution and disables certain automatic performance optimizations.

Warning

A ValueRangeProvider on the planning entity is not currently compatible with a chained variable.

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

    @ValueRangeProvider(id = "delayRange")
    public CountableValueRange<Integer> getDelayRange() {
        return ValueRangeFactory.createIntValueRange(0, 5000);
    }

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

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

    @ValueRangeProvider(id = "stockAmountRange")
    public CountableValueRange<Integer> getStockAmountRange() {
         // Range: 0, 200, 400, 600, ..., 9999600, 9999800, 10000000
        return ValueRangeFactory.createIntValueRange(0, 10000000, 200);
    }

Note

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

The ValueRangeFactory has creation methods for several value class types:

Value range providers can be combined, for example:

    @PlanningVariable(valueRangeProviderRefs = {"companyCarRange", "personalCarRange"})
    public Car getCar() {
        return car;
    }
    @ValueRangeProvider(id = "companyCarRange")
    public List<CompanyCar> getCompanyCarList() {
        return companyCarList;
    }

    @ValueRangeProvider(id = "personalCarRange")
    public List<PersonalCar> getPersonalCarList() {
        return personalCarList;
    }

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

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

    @PlanningVariable(..., strengthComparatorClass = CloudComputerStrengthComparator.class)
    public CloudComputer getComputer() {
        // ...
    }
public class CloudComputerStrengthComparator implements Comparator<CloudComputer> {

    public int compare(CloudComputer a, CloudComputer b) {
        return new CompareToBuilder()
                .append(a.getMultiplicand(), b.getMultiplicand())
                .append(b.getCost(), a.getCost()) // Descending (but this is debatable)
                .append(a.getId(), b.getId())
                .toComparison();
    }

}

Note

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

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

    @PlanningVariable(..., strengthWeightFactoryClass = RowStrengthWeightFactory.class)
    public Row getRow() {
        // ...
    }

See sorted selection for more information.

Important

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

None of the current planning variable state in any of the planning entities should be used to compare planning values. During construction heuristics, those variables are likely to be null. 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:

Warning

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

Note

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

The optimization algorithms and built-in Moves do chain correction to guarantee that the model stays valid:

Warning

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

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

public class Domicile ... implements Standstill {

    ...

    public City getCity() {...}

}

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

public interface Standstill {

    City getCity();

}

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

@PlanningEntity
public class Visit ... implements Standstill {

    ...

    public City getCity() {...}

    @PlanningVariable(graphType = PlanningVariableGraphType.CHAINED,
        valueRangeProviderRefs = {"domicileRange", "visitRange"})
    public Standstill getPreviousStandstill() {
        return previousStandstill;
    }

    public void setPreviousStandstill(Standstill previousStandstill) {
        this.previousStandstill = previousStandstill;
    }

}

Notice how two value range providers are usually combined:

A shadow variable is a variable whose 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 two locations).

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

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.

There are several build-in shadow variables:

Two variables are bi-directional if their instances always point to each other (unless one side points to null and the other side does not exist). So if A references B, then B references A.

For a non-chained planning variable, the bi-directional relationship must be a many to one relationship. To map a bi-directional relationship between two planning variables, annotate the master side (which is the genuine side) as a normal planning variable:

@PlanningEntity
public class CloudProcess {

    @PlanningVariable(...)
    public CloudComputer getComputer() {
        return computer;
    }
    public void setComputer(CloudComputer computer) {...}

}

And then annotate the other side (which is the shadow side) with a @InverseRelationShadowVariable annotation on a Collection (usually a Set or List) property:

@PlanningEntity
public class CloudComputer {

    @InverseRelationShadowVariable(sourceVariableName = "computer")
    public List<CloudProcess> getProcessList() {
        return processList;
    }

}

The sourceVariableName property is the name of the genuine planning variable on the return type of the getter (so the name of the genuine planning variable on the other side).

Note

The shadow property, which is a Collection, can never be null. If no genuine variable is referencing that shadow entity, then it is an empty Collection. Furthermore it must be a mutable Collection because once the Solver starts initializing or changing genuine planning variables, it will add and remove to the Collections of those shadow variables accordingly.

For a chained planning variable, the bi-directional relationship must be a one to one relationship. In that case, the genuine side looks like this:

@PlanningEntity
public class Customer ... {

    @PlanningVariable(graphType = PlanningVariableGraphType.CHAINED, ...)
    public Standstill getPreviousStandstill() {
        return previousStandstill;
    }
    public void setPreviousStandstill(Standstill previousStandstill) {...}

}

And the shadow side looks like this:

@PlanningEntity
public class Standstill {

    @InverseRelationShadowVariable(sourceVariableName = "previousStandstill")
    public Customer getNextCustomer() {
         return nextCustomer;
    }
    public void setNextCustomer(Customer nextCustomer) {...}

}

Warning

The input planning problem of a Solver must not violate bi-directional relationships. If A points to B, then B must point to A. Planner will not violate that principle during planning, but the input must not violate it.

An anchor shadow variable is the anchor of a chained variable.

Annotate the anchor property as a @AnchorShadowVariable annotation:

@PlanningEntity
public class Customer {

    @AnchorShadowVariable(sourceVariableName = "previousStandstill")
    public Vehicle getVehicle() {...}
    public void setVehicle(Vehicle vehicle) {...}

}

The sourceVariableName property is the name of the chained variable on the same entity class.

To update a shadow variable, Planner uses a VariableListener. To define a custom shadow variable, write a custom VariableListener: implement the interface and annotate it on the shadow variable 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 at least one 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.

If one VariableListener changes two shadow variables (because having two separate VariableListeners would be inefficient), then annotate only the first shadow variable with the variableListenerClass and let the other shadow variable(s) reference the first shadow variable:

    @PlanningVariable(...)
    public Standstill getPreviousStandstill() {
        return previousStandstill;
    }

    @CustomShadowVariable(variableListenerClass = TransportTimeAndCapacityUpdatingVariableListener.class,
            sources = {@CustomShadowVariable.Source(variableName = "previousStandstill")})
    public Integer getTransportTime() {
        return transportTime;
    }

    @CustomShadowVariable(variableListenerRef = @PlanningVariableReference(variableName = "transportTime"))
    public Integer getCapacity() {
        return capacity;
    }

All shadow variables are triggered by a VariableListener, regardless if it's a build-in or a custom shadow variable. The genuine and shadow variables form a graph, that determines the order in which the afterEntityAdded(), afterVariableChanged() and afterEntityRemoved() methods are called:

Note

In the example above, D could have also been ordered after E (or F) because there is no direct or indirect dependency between D and E (or F).

Planner guarantees that:

Planner does not guarantee the order in which the after*() methods are called for the same VariableListener with different parameters (such as A1 and A2 in the example above), although they are likely to be in the order in which they were affected.

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

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

You need to present the problem as a Solution instance to the Solver. So your class needs to implement the Solution interface:

public interface Solution<S extends Score> {

    S getScore();
    void setScore(S score);

    Collection<? extends Object> getProblemFacts();

}

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

@PlanningSolution
public class NQueens implements Solution<SimpleScore> {

    private int n;

    // Problem facts
    private List<Column> columnList;
    private List<Row> rowList;

    // Planning entities
    private List<Queen> queenList;

    // ...
}

A planning solution class also needs to be annotated with the @PlanningSolution annotation. Without automated scanning, the solver configuration also needs to declare the planning solution class:

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

Planner needs to extract the entity instances from the Solution instance. It gets those collection(s) by calling every getter (or field) that is annotated with @PlanningEntityCollectionProperty:

@PlanningSolution
public class NQueens implements Solution<SimpleScore> {
    ...

    private List<Queen> queenList;

    @PlanningEntityCollectionProperty
    public List<Queen> getQueenList() {
        return queenList;
    }

}

There can be multiple @PlanningEntityCollectionProperty annotated members. Those can even return a Collection with the same entity class type.

Note

A @PlanningEntityCollectionProperty annotation needs to be on a member in a class with a @PlanningSolution annotation. It is ignored on parent classes or subclasses without that annotation.

In rare cases, a planning entity might be a singleton: use @PlanningEntityProperty on its getter (or field) instead.

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

@PlanningSolution
public class NQueens implements Solution<SimpleScore> {

    private SimpleScore score;

    public SimpleScore getScore() {
        return score;
    }

    public void setScore(SimpleScore score) {
        this.score = score;
    }

    // ...
}

Most use cases use a HardSoftScore instead:

@PlanningSolution
public class CourseSchedule implements Solution<HardSoftScore> {

    private HardSoftScore score;

    public HardSoftScore getScore() {
        return score;
    }

    public void setScore(HardSoftScore score) {
        this.score = score;
    }

    // ...
}

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

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

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

    public Collection<? extends Object> getProblemFacts() {
        List<Object> facts = new ArrayList<Object>();
        facts.addAll(columnList);
        facts.addAll(rowList);
        // Do not add the planning entity's (queenList) because that will be done automatically
        return facts;
    }

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

Note

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

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

A cached problem fact is a problem fact that does not exist in the real domain model, but is calculated before the Solver really starts solving. The getProblemFacts() method 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 two Topics which share at least one 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;
    }

Where a score constraint needs to check that no two exams with a topic that shares a student are scheduled close together (depending on the constraint: at the same time, in a row, or in the same day), the TopicConflict instance can be used as a problem fact, rather than having to combine every two 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.

Note

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

A planning clone of a Solution must fulfill these requirements:

Implementing a planning clone method is hard, therefore you do not need to implement it.

This SolutionCloner is used by default. It works well for most use cases.

Warning

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

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

@DeepPlanningClone
public class SeatDesignationDependency {
    private SeatDesignation leftSeatDesignation; // planning entity
    private SeatDesignation rightSeatDesignation; // planning entity
    ...
}

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

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

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

public interface PlanningCloneable<T> {

    T planningClone();

}

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

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

    /**
     * Clone will only deep copy the {@link #queenList}.
     */
    public NQueens planningClone() {
        NQueens clone = new NQueens();
        clone.id = id;
        clone.n = n;
        clone.columnList = columnList;
        clone.rowList = rowList;
        List<Queen> clonedQueenList = new ArrayList<Queen>(queenList.size());
        for (Queen queen : queenList) {
            clonedQueenList.add(queen.planningClone());
        }
        clone.queenList = clonedQueenList;
        clone.score = score;
        return clone;
    }
}

The planningClone() method should only deep clone the planning entities. Notice that the problem facts, such as Column and Row are not normally cloned: even their List instances are not cloned. If you were to clone the problem facts too, then you would 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 were to 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 its variable must point to the clone of B, not the original B.

Create a Solution instance to represent your planning problem's dataset, so it can be set 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 = 0L;
        for (Column column : nQueens.getColumnList()) {
            Queen queen = new Queen();
            queen.setId(id);
            id++;
            queen.setColumn(column);
            // Notice that we leave the PlanningVariable properties on null
            queenList.add(queen);
        }
        return queenList;
    }

Figure 4.1. Uninitialized Solution for the 4 Queens Puzzle

Uninitialized Solution for the 4 Queens Puzzle

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

        private void createLectureList(CourseSchedule schedule) {
            List<Course> courseList = schedule.getCourseList();
            List<Lecture> lectureList = new ArrayList<Lecture>(courseList.size());
            long id = 0L;
            for (Course course : courseList) {
                for (int i = 0; i < course.getLectureSize(); i++) {
                    Lecture lecture = new Lecture();
                    lecture.setId(id);
                    id++;
                    lecture.setCourse(course);
                    lecture.setLectureIndexInCourse(i);
                    // Notice that we leave the PlanningVariable properties (period and room) on null
                    lectureList.add(lecture);
                }
            }
            schedule.setLectureList(lectureList);
        }

4.4. Use the Solver

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)

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.

Note

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

The Solution instance returned by the methods solve(Solution) or getBestSolution() is most likely a planning clone of the instance given to the method solve(Solution), which implies it is a different instance.

Note

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

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 FULL_ASSERT mode turns on all assertions (such as assert that the incremental score calculation is uncorrupted for each move) to fail-fast on a bug in a Move implementation, a score rule, the rule engine itself, ...

This mode is reproducible (see the reproducible mode). It is also intrusive because it calls the method calculateScore() more frequently than a non-assert mode.

The FULL_ASSERT mode is horribly slow (because it does not rely on incremental score calculation).

The NON_INTRUSIVE_FULL_ASSERT turns on several assertions to fail-fast on a bug in a Move implementation, a score rule, the rule engine itself, ...

This mode is reproducible (see the reproducible mode). It is non-intrusive because it does not call the method calculateScore() more frequently than a non assert mode.

The NON_INTRUSIVE_FULL_ASSERT mode is horribly slow (because it does not rely on incremental score calculation).

The FAST_ASSERT mode turns on most assertions (such as assert that an undoMove's score is the same as before the Move) to fail-fast on a bug in a Move implementation, a score rule, the rule engine itself, ...

This mode is reproducible (see the reproducible mode). It is also intrusive because it calls the method calculateScore() more frequently than a non assert mode.

The FAST_ASSERT mode is slow.

It is recommended to write a test case that does a short run of your planning problem with the FAST_ASSERT mode on.

The reproducible mode is the default mode because it is recommended during development. In this mode, two runs in the same Planner version will execute the same code in the same order. Those two runs will have the same result at every step, except if the note below applies. This enables you to reproduce bugs consistently. It also allows you to benchmark certain refactorings (such as a score constraint performance optimization) fairly across runs.

Note

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

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

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

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

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

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>

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);
        Solution bestSolution = solver.solve(planningProblem);
        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>

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.