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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
4.3. 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

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

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

       SolverFactory solverFactory = SolverFactory.createFromXmlResource(
       Solver solver = solverFactory.buildSolver();

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

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

A solver configuration file looks like this:

<?xml version="1.0" encoding="UTF-8"?>
  <!-- Define the model -->

  <!-- Define the score function -->

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

Notice the 3 parts in it:

  • Define the model

  • Define the score function

  • Configure the optimization algorithm(s)

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

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

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

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


In real-time planning, even thought the problem itself changes, problem facts don't 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's 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 1 Employee has multiple ShiftAssignments but 1 ShiftAssignment has only 1 Employee.

  • A planning entity class should have at least 1 problem property. A planning entity class with only planning variables can normally be simplified by converting 1 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 that at least 1 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 2 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 in doubt 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's impossible to accurately predict how many EmployeeAssignment instances to make per Employee.

For inspiration, take a look at how the examples modeled their domain:


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

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

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

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

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

public class Queen {

    private Column column;

    // Planning variables: changes during planning, between score calculations.
    private Row row;

    // ... getters and setters

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

public class Lecture {

    private Course course;
    private int lectureIndexInCourse;

    // Planning variables: changes during planning, between score calculations.
    private Period period;
    private Room room;

    // ...

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


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

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

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

@PlanningEntity(difficultyComparatorClass = CloudProcessDifficultyComparator.class)
public class CloudProcess {
    // ...
public class CloudProcessDifficultyComparator implements Comparator<CloudProcess> {

    public int compare(CloudProcess a, CloudProcess b) {
        return new CompareToBuilder()
                .append(a.getRequiredMultiplicand(), b.getRequiredMultiplicand())
                .append(a.getId(), b.getId())


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

@PlanningEntity(difficultyWeightFactoryClass = QueenDifficultyWeightFactory.class)
public class Queen {
    // ...

See sorted selection for more information.


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

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

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

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).

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

Every entity has its own List instance, except if multiple entity 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.

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())


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.


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

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

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

A planning variable that is chained either:

Here are some example of valid and invalid chains:

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

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

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

public class Domicile ... implements Standstill {


    public City getCity() {...}


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

public interface Standstill {

    City getCity();


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

public class Visit ... implements Standstill {


    public City getCity() {...}

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

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


Notice how 2 value range providers are usually combined:

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

Annotate the anchor property as a @AnchorShadowVariable annotation:

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.

    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).

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");
            scoreDirector.afterVariableChanged(shadowCustomer, "vehicle");
            shadowCustomer = shadowCustomer.getNextCustomer();


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

A planning clone of a Solution must fulfill these requirements:

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

If your Solution implements PlanningCloneable, Planner will automatically choose to clone it by calling the 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) {
        clone.queenList = clonedQueenList;
        clone.score = score;
        return clone;

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


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

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();
        return nQueens;

    private List<Queen> createQueenList(NQueens nQueens) {
        int n = nQueens.getN();
        List<Queen> queenList = new ArrayList<Queen>(n);
        long id = 0;
        for (Column column : nQueens.getColumnList()) {
            Queen queen = new Queen();
            // Notice that we leave the PlanningVariable properties on null
        return queenList;

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

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

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:


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

All time spent values are in milliseconds.

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

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


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


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



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


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" />




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:

        Solution bestSolution = solver.getBestSolution();

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