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

Interface ScoreBounder

All Known Implementing Classes:

TrendBasedScoreBounder

public interface ScoreBounder

Method Summary

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

Method Details

calculateOptimisticBound

Score calculateOptimisticBound(ScoreDirector scoreDirector,
 Score score)

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.

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

Score calculatePessimisticBound(ScoreDirector scoreDirector,
 Score score)

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.

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)