Package ai.timefold.solver.core.impl.exhaustivesearch.node.bounder

Class TrendBasedScoreBounder<Score_ extends Score<Score_>>

java.lang.Object

ai.timefold.solver.core.impl.exhaustivesearch.node.bounder.TrendBasedScoreBounder<Score_>

All Implemented Interfaces:

ScoreBounder<Score_>

public final class TrendBasedScoreBounder<Score_ extends Score<Score_>>
extends Object
implements ScoreBounder<Score_>

Constructor Summary

Constructors

Constructor	Description
TrendBasedScoreBounder(ScoreDefinition<Score_> scoreDefinition, InitializingScoreTrend initializingScoreTrend)

Method Summary

Modifier and Type	Method	Description
InnerScore<Score_>	calculateOptimisticBound(ScoreDirector<?> scoreDirector, InnerScore<Score_> score)	In OR terms, this is called the lower bound if they minimize, and upper bound if they maximize.
InnerScore<Score_>	calculatePessimisticBound(ScoreDirector<?> scoreDirector, InnerScore<Score_> score)	In OR terms, this is called the upper bound if they minimize, and lower bound if they maximize.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

TrendBasedScoreBounder

public TrendBasedScoreBounder(ScoreDefinition<Score_> scoreDefinition,
 InitializingScoreTrend initializingScoreTrend)

Method Details

calculateOptimisticBound

public InnerScore<Score_> calculateOptimisticBound(ScoreDirector<?> scoreDirector,
 InnerScore<Score_> score)

Description copied from interface: ScoreBounder

In OR terms, this is called the lower bound if they minimize, and upper bound if they maximize. Because we always maximize the Score, calling it lower bound would be a contradiction.

Specified by:

calculateOptimisticBound in interface ScoreBounder<Score_ extends Score<Score_>>

Parameters:

scoreDirector - never null, use ScoreDirector.getWorkingSolution() to get the working PlanningSolution

score - never null, the Score of the working PlanningSolution

Returns:

never null, never worse than the best possible Score we can get by initializing the uninitialized variables of the working PlanningSolution.

See Also:

• ScoreDefinition.buildOptimisticBound(InitializingScoreTrend, Score)

calculatePessimisticBound

public InnerScore<Score_> calculatePessimisticBound(ScoreDirector<?> scoreDirector,
 InnerScore<Score_> score)

Description copied from interface: ScoreBounder

In OR terms, this is called the upper bound if they minimize, and lower bound if they maximize. Because we always maximize the Score, calling it upper bound would be a contradiction.

Specified by:

calculatePessimisticBound in interface ScoreBounder<Score_ extends Score<Score_>>

Parameters:

scoreDirector - never null, use ScoreDirector.getWorkingSolution() to get the working PlanningSolution

score - never null, the Score of the working PlanningSolution

Returns:

never null, never better than the worst possible Score we can get by initializing the uninitialized variables of the working PlanningSolution.

See Also:

• ScoreDefinition.buildPessimisticBound(InitializingScoreTrend, Score)