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## Chapter 4. Planner configuration

## 4.1. Overview

Solving a planning problem with OptaPlanner 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. Set it as the planning problem on the `Solver` with `Solver.setPlanningProblem(...)`.

4. Solve it with `Solver.solve()`.

5. Get the best solution found by the `Solver` with `Solver.getBestSolution()`.

## 4.2. Solver configuration

### 4.2.1. Solver configuration by XML file

You can build a `Solver` instance with the `XmlSolverFactory`. Configure it with a solver configuration XML file:

           XmlSolverFactory solverFactory = new XmlSolverFactory(

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

           Solver solver = solverFactory.buildSolver();

A solver configuration file looks something like this:

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

    <solver>

      <!-- Define the model -->

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

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

      <!-- Define the score function -->

      <scoreDirectorFactory>

        <scoreDefinitionType>SIMPLE</scoreDefinitionType>

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

      </scoreDirectorFactory>

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

      <termination>

        ...

      </termination>

      <constructionHeuristic>

        ...

      </constructionHeuristic>

      <localSearch>

        ...

      </localSearch>

    </solver>

Notice the 3 parts in it:

- Define the model

- Define the score function

- Configure the optimization algorithm(s)

We'll explain these various parts of a configuration later in this manual.

OptaPlanner makes it relatively easy to switch optimization algorithm(s) just by changing the configuration. There's even a `Benchmark` utility which allows you to play out different configurations against each other and report the most appropriate configuration for your problem. You could for example play out tabu search versus simulated annealing, on 4 queens and 64 queens.

### 4.2.2. Solver configuration by Java API

As an alternative to the XML file, a solver configuration can also be configured with the `SolverConfig` API:

            SolverConfig solverConfig = new SolverConfig();

            solverConfig.setSolutionClass(NQueens.class);

            solverConfig.setPlanningEntityClassList(Collections.<Class<?>>singletonList(Queen.class));

            ScoreDirectorFactoryConfig scoreDirectorFactoryConfig = new ScoreDirectorFactoryConfig();

            scoreDirectorFactoryConfig.setScoreDefinitionType(ScoreDirectorFactoryConfig.ScoreDefinitionType.SIMPLE);

            scoreDirectorFactoryConfig.setScoreDrlList(

                    Arrays.asList("/org/optaplanner/examples/nqueens/solver/nQueensScoreRules.drl"));

            solverConfig.setScoreDirectorFactoryConfig(scoreDirectorFactoryConfig);

            TerminationConfig terminationConfig = new TerminationConfig();

            // ...

            solverConfig.setTerminationConfig(terminationConfig);

            List<SolverPhaseConfig> solverPhaseConfigList = new ArrayList<SolverPhaseConfig>();

            ConstructionHeuristicSolverPhaseConfig constructionHeuristicSolverPhaseConfig

                    = new ConstructionHeuristicSolverPhaseConfig();

            // ...

            solverPhaseConfigList.add(constructionHeuristicSolverPhaseConfig);

            LocalSearchSolverPhaseConfig localSearchSolverPhaseConfig = new LocalSearchSolverPhaseConfig();

            // ...

            solverPhaseConfigList.add(localSearchSolverPhaseConfig);

            solverConfig.setSolverPhaseConfigList(solverPhaseConfigList);

            Solver solver = solverConfig.buildSolver();

It is highly recommended to configure by XML file instead of this API. To dynamically configure a value at runtime, use the XML file as a template and extract the `SolverConfig` class with `getSolverConfig()` to configure the dynamic value at runtime:

            XmlSolverFactory solverFactory = new XmlSolverFactory();

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

            SolverConfig solverConfig = solverFactory.getSolverConfig();

            solverConfig.getTerminationConfig().setMaximumMinutesSpend(userInput);

            Solver solver = solverConfig.buildSolver();

## 4.3. Model your planning problem

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

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

- A unrelated class: not used by any of the score constraints. From a planning standpoint, this data is obsolete.

- A problem fact class: used by the score constraints, but does NOT change during planning (as long as the problem stays the same). For example: `Bed`, `Room`, `Shift`, `Employee`, `Topic`, `Period`, ...

- A planning entity class: used by the score constraints and changes during planning. For example: `BedDesignation`, `ShiftAssignment`, `Exam`, ...

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

## Note

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

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

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

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

### 4.3.2. Problem fact

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

    public class Column implements Serializable {

        private int index;

        // ... getters

    }

    public class Row implements Serializable {

        private int index;

        // ... getters

    }

A problem fact can reference other problem facts of course:

    public class Course implements Serializable {

        private String code;

        private Teacher teacher; // Other problem fact

        private int lectureSize;

        private int minWorkingDaySize;

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

        private int studentSize;

        // ... getters

    }

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

## Note

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

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

### 4.3.3. Planning entity

#### 4.3.3.1. Planning entity annotation

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

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

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

    @PlanningEntity

    public class Queen {

        private Column column;

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

        private Row row;

        // ... getters and setters

    }

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

    @PlanningEntity

    public class Lecture {

        private Course course;

        private int lectureIndexInCourse;

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

        private Period period;

        private Room room;

        // ...

    }

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

    <solver>

      ...

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

      ...

    </solver>

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

## Note

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

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

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

#### 4.3.3.2. Planning entity difficulty

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

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

    @PlanningEntity(difficultyComparatorClass = CloudProcessDifficultyComparator.class)

    public class CloudProcess {

        // ...

    }

    public class CloudProcessDifficultyComparator implements Comparator<CloudProcess> {

        public int compare(CloudProcess a, CloudProcess b) {

            return new CompareToBuilder()

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

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

                    .toComparison();

        }

    }

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

    @PlanningEntity(difficultyWeightFactoryClass = QueenDifficultyWeightFactory.class)

    public class Queen {

        // ...

    }

See Sorted Selection for more information.

## Important

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

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

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

### 4.3.4. Planning variable

#### 4.3.4.1. Planning variable annotation

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

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

    @PlanningEntity

    public class Queen {

        private Row row;

        // ...

        @PlanningVariable(valueRangeProviderRefs = {"rowRange"})

        public Row getRow() {

            return row;

        }

        public void setRow(Row row) {

            this.row = row;

        }

    }

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

#### 4.3.4.2. Nullable planning variable

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

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

        @PlanningVariable(..., nullable = true)

        public Worker getWorker() {

            return worker;

        }

## Important

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

## Note

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

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

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

        public Worker getWorker() {

            return worker;

        }

#### 4.3.4.3. When is a planning variable considered initialized?

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

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

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

### 4.3.5. Planning value and planning value ranges

#### 4.3.5.1. Planning value

A planning value is a possible value for a planning variable. Usually, a planning value is a problem fact, but it can also be any object, for example a double. It can even be another planning entity or even a interface implemented by 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 discrete (for example row `1`, `2`, `3` or `4`) or continuous (for example any `double` between `0.0` and `1.0`).

There are several ways to define the value range of a planning variable with the `@ValueRangeProvider` annotation. A `@ValueRangeProvider` annotation always has a property `id`, which is referenced by the `@PlanningVariable`'s property `valueRangeProviderRefs`.

#### 4.3.5.2. Planning value range provider

##### 4.3.5.2.1. `ValueRangeProvider` on a `Solution`

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

The `Solution` implementation has method which returns a `Collection`. 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;

        }

    }

That `Collection` must not contain the value `null`, even for a nullable planning variable.

##### 4.3.5.2.2. `ValueRangeProvider` on a planning entity

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

        @PlanningVariable(valueRangeProviderRefs = {"possibleRoomRange"})

        public Room getRoom() {

            return room;

        }

        @ValueRangeProvider(id = "possibleRoomRange")

        public List<Room> getPossibleRoomList() {

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

        }

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

## Note

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

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

This value range is not (yet) compatible with a chained variable.

##### 4.3.5.2.3. Combining ValueRangeProviders

Value range providers can be combined, for example:

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

        public Car getCar() {

            return car;

        }

        @ValueRangeProvider(id = "companyCarRange")

        public List<CompanyCar> getCompanyCarList() {

            return companyCarList;

        }

        @ValueRangeProvider(id = "personalCarRange")

        public List<PersonalCar> getPersonalCarList() {

            return personalCarList;

        }

#### 4.3.5.3. Planning value strength

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

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

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

        public CloudComputer getComputer() {

            // ...

        }

    public class CloudComputerStrengthComparator implements Comparator<CloudComputer> {

        public int compare(CloudComputer a, CloudComputer b) {

            return new CompareToBuilder()

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

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

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

                    .toComparison();

        }

    }

## Note

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

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

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

        public Row getRow() {

            // ...

        }

See Sorted Selection for more information.

## Important

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

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

#### 4.3.5.4. Chained planning variable

Some use cases, such as TSP and Vehicle Routing, require chaining. This means the planning entities point to each other and form a chain.

A planning variable that is chained either:

- Directly points to a planning fact, which is called an anchor.

- Points to another planning entity with the same planning variable, which recursively points to an anchor.

Here are some example of valid and invalid chains:

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

- A chain is never a loop. The tail is always open.

- Every chain always has exactly 1 anchor. The anchor is a problem fact, never a planning entity.

- A chain is never a tree, it is always a line. Every anchor or planning entity has at most 1 trailing planning entity.

- Every initialized planning entity is part of a chain.

- An anchor with no planning entities pointing to it, is also considered a chain.

## Warning

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

## Note

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

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

## Warning

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

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

    public class Domicile ... implements Standstill {

        ...

        public City getCity() {...}

    }

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

    public interface Standstill {

        City getCity();

    }

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

    @PlanningEntity

    public class Visit ... implements Standstill {

        ...

        public City getCity() {...}

        @PlanningVariable(chained = true, valueRangeProviderRefs = {"domicileRange", "visitRange"})

        public Standstill getPreviousStandstill() {

            return previousStandstill;

        }

        public void setPreviousStandstill(Standstill previousStandstill) {

            this.previousStandstill = previousStandstill;

        }

    }

Notice how 2 value range providers are usually combined:

- The value range provider which holds the anchors, for example `domicileList`.

- The value range provider which holds the initialized planning entities, for example `visitList`.

#### 4.3.5.5. Bi-directional variable

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

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

    @PlanningEntity

    public class Customer ... {

        @PlanningVariable(chained = true, ...)

        public Standstill getPreviousStandstill() {

            return previousStandstill;

        }

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

    }

And then annotate the other side as a `@PlanningVariable` with only a `mappedBy` annotation (and no `valueRangeProviderRefs`).

    @PlanningEntity

    public interface Standstill {

        @PlanningVariable(mappedBy = "previousStandstill")

        Customer getNextCustomer();

        void setNextCustomer(Customer nextCustomer);

    }

The `mappedBy` variable is a form of a shadow variable: Planner uses a build-in `PlanningVariableListener` to update its state.

#### 4.3.5.6. Planning variable listener that updates shadow variables

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

To use custom `PlanningVariableListener`, implement the interface and annotate it on the genuine planning variable(s) that trigger changes in the shadow variable(s):

        @PlanningVariable(..., variableListenerClasses = {VehicleUpdatingVariableListener.class, ArrivalTimeUpdatingVariableListener.class})

        public Standstill getPreviousStandstill() {

            return previousStandstill;

        }

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

    public class VehicleUpdatingVariableListener implements PlanningVariableListener<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();

            }

        }

    }

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

## Warning

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

## Warning

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

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

### 4.3.6. Planning problem and planning solution

#### 4.3.6.1. Planning problem instance

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

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

#### 4.3.6.2. The `Solution` interface

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

    public interface Solution<S extends Score> {

        S getScore();

        void setScore(S score);

        Collection<? extends Object> getProblemFacts();

    }

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

    public class NQueens implements Solution<SimpleScore> {

        private int n;

        // Problem facts

        private List<Column> columnList;

        private List<Row> rowList;

        // Planning entities

        private List<Queen> queenList;

        // ...

    }

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

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

    public class NQueens implements Solution<SimpleScore> {

        private SimpleScore score;

        public SimpleScore getScore() {

            return score;

        }

        public void setScore(SimpleScore score) {

            this.score = score;

        }

        // ...

    }

Most use cases use a `HardSoftScore` instead:

    public class CourseSchedule implements Solution<HardSoftScore> {

        private HardSoftScore score;

        public HardSoftScore getScore() {

            return score;

        }

        public void setScore(HardSoftScore score) {

            this.score = score;

        }

        // ...

    }

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

#### 4.3.6.4. The `getProblemFacts()` method

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

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

        public Collection<? extends Object> getProblemFacts() {

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

            facts.addAll(columnList);

            facts.addAll(rowList);

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

            return facts;

        }

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

## Note

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

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

#### 4.3.6.5. Cached problem fact

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

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

        public Collection<? extends Object> getProblemFacts() {

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

            // ...

            facts.addAll(calculateTopicConflictList());

            // ...

            return facts;

        }

        private List<TopicConflict> calculateTopicConflictList() {

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

            for (Topic leftTopic : topicList) {

                for (Topic rightTopic : topicList) {

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

                        int studentSize = 0;

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

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

                                studentSize++;

                            }

                        }

                        if (studentSize > 0) {

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

                        }

                    }

                }

            }

            return topicConflictList;

        }

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

#### 4.3.6.6. Cloning a `Solution`

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

## Note

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

A planning clone of a `Solution` must fulfill these requirements:

- The clone must represent the same planning problem. Usually it reuses the same instances of the problem facts and problem fact collections as the original.

- The clone must use different, cloned instances of the entities and entity collections. Changes to an original `Solution`'s entity's variables must not effect its clone.

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

##### 4.3.6.6.1. `FieldAccessingSolutionCloner`

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

## Warning

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

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

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

    public interface PlanningCloneable<T> {

        T planningClone();

    }

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

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

        ...

        /**

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

         */

        public NQueens planningClone() {

            NQueens clone = new NQueens();

            clone.id = id;

            clone.n = n;

            clone.columnList = columnList;

            clone.rowList = rowList;

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

            for (Queen queen : queenList) {

                clonedQueenList.add(queen.planningClone());

            }

            clone.queenList = clonedQueenList;

            clone.score = score;

            return clone;

        }

    }

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

## Warning

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

#### 4.3.6.7. Build an uninitialized solution

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

        private NQueens createNQueens(int n) {

            NQueens nQueens = new NQueens();

            nQueens.setId(0L);

            nQueens.setN(n);

            nQueens.setColumnList(createColumnList(nQueens));

            nQueens.setRowList(createRowList(nQueens));

            nQueens.setQueenList(createQueenList(nQueens));

            return nQueens;

        }

        private List<Queen> createQueenList(NQueens nQueens) {

            int n = nQueens.getN();

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

            long id = 0;

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

                Queen queen = new Queen();

                queen.setId(id);

                id++;

                queen.setColumn(column);

                // Notice that we leave the PlanningVariable properties on null

                queenList.add(queen);

            }

            return queenList;

        }

Figure 4.1. Uninitialized solution for the 4 queens puzzle

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

            private void createLectureList(CourseSchedule schedule) {

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

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

                for (Course course : courseList) {

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

                        Lecture lecture = new Lecture();

                        lecture.setCourse(course);

                        lecture.setLectureIndexInCourse(i);

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

                        lectureList.add(lecture);

                    }

                }

                schedule.setLectureList(lectureList);

            }

## 4.4. Use the `Solver`

### 4.4.1. The Solver interface

A `Solver` implementation will solve your planning problem.

    public interface Solver {

        void setPlanningProblem(Solution planningProblem);

        void solve();

        Solution getBestSolution();

        // ...

    }

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

### 4.4.2. Solving a problem

Solving a problem is quite easy once you have:

- A `Solver` build from a solver configuration

- A `Solution` that represents the planning problem instance

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

        solver.setPlanningProblem(planningProblem);

        solver.solve();

        Solution bestSolution = solver.getBestSolution();

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

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

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

## Note

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

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

## Note

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

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

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

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

    <solver>

      <environmentMode>FAST_ASSERT</environmentMode>

      ...

    </solver>

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

There are 4 environment modes:

#### 4.4.3.1. FULL_ASSERT

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 doesn't rely on delta based score calculation).

#### 4.4.3.2. NON_INTRUSIVE_FULL_ASSERT

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 doesn't rely on delta based score calculation).

#### 4.4.3.3. FAST_ASSERT

The FAST_ASSERT mode turns on most assertions (such as assert that an undo Move'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's recommended to write a test case which does a short run of your planning problem with the FAST_ASSERT mode on.

#### 4.4.3.4. REPRODUCIBLE (default)

The reproducible mode is the default mode because it is recommended during development. In this mode, 2 runs in the same Planner version will execute the same code in the same order. Those 2 runs will have the same result, except if the note below applies. This allows you to consistently reproduce bugs. It also allows you to benchmark certain refactorings (such as a score constraint optimization) fairly across runs.

## Note

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

- Use of `HashSet` (or another `Collection` which has an inconsistent order between JVM runs) for collections of planning entities or planning values (but not normal problem facts), especially in the `Solution` implementation. Replace it with `LinkedHashSet`.

- Combining a time gradient dependent algorithms (most notably simulated annealing) together with time spend termination. A sufficiently large difference in allocated CPU time will influence the time gradient values. Replace simulated annealing with late acceptance. Or instead, replace time spend termination with step count termination.

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

In practice, this mode uses the default random seed, and it also disables certain concurrency optimizations (such as work stealing).

#### 4.4.3.5. PRODUCTION

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

The random seed is different on every run, which makes it more robust against an unlucky random seed. An unlucky random seed gives a bad result on a certain data set with a certain solver configuration. Note that in most use cases the impact of the random seed is relatively low on the result (even with simulated annealing). An occasional bad result is far more likely to be caused by another issue (such as a score trap).

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

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

- error: Log errors, except those that are thrown to the calling code as a `RuntimeException`.

  ## Note

  If an error happens, Planner normally fails fast: it throws a subclass of `RuntimeException` with a detailed message to the calling code. It does not log it as an error itself to avoid duplicate log messages. Except if the calling code explicitly catches and eats that `RuntimeException`, a `Thread`'s default `ExceptionHandler` will log it as an error anyway. Meanwhile, the code is disrupted from doing further harm or obfuscating the error.

- warn: Log suspicious circumstances.

- info: Log every phase and the solver itself. See scope overview.

- debug: Log every step of every phase. See scope overview.

- trace: Log every move of every step of every phase. See scope overview.

  ## Note

  Turning on `trace` logging, will slow down performance considerably: it's often 4 times slower. However, it's invaluable during development to discover a bottleneck.

  Even debug logging can slow down performance considerably for fast stepping algorithms (such as Late Acceptance and Simulated Annealing), but not for slow stepping algorithms (such as Tabu Search).

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

    INFO  Solving started: time spend (0), score (null), new best score (null), random seed (0).
    DEBUG     Step index (0), time spend (1), score (0), initialized planning entity (col2@row0).
    DEBUG     Step index (1), time spend (3), score (0), initialized planning entity (col1@row2).
    DEBUG     Step index (2), time spend (4), score (0), initialized planning entity (col3@row3).
    DEBUG     Step index (3), time spend (5), score (-1), initialized planning entity (col0@row1).
    INFO  Phase (0) constructionHeuristic ended: step total (4), time spend (6), best score (-1).
    DEBUG     Step index (0), time spend (10), score (-1),     best score (-1), accepted/selected move count (12/12) for picked step (col1@row2 => row3).
    DEBUG     Step index (1), time spend (12), score (0), new best score (0), accepted/selected move count (12/12) for picked step (col3@row3 => row2).
    INFO  Phase (1) localSearch ended: step total (2), time spend (13), best score (0).
    INFO  Solving ended: time spend (13), best score (0), average calculate count per second (4846).

All time spends are in milliseconds.

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

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

        <dependency>

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

          <artifactId>logback-classic</artifactId>

          <version>1.x</version>

        </dependency>

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

    <configuration>

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

      ...

    <configuration>

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

        <dependency>

          <groupId>org.slf4j</groupId>

          <artifactId>slf4j-log4j12</artifactId>

          <version>1.x</version>

        </dependency>

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

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

      <category name="org.optaplanner">

        <priority value="debug" />

      </category>

      ...

    </log4j:configuration>

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