discrete_optimization.generic_tasks_tools.transformations package

Submodules

discrete_optimization.generic_tasks_tools.transformations.generic_scheduling_impl module

class discrete_optimization.generic_tasks_tools.transformations.generic_scheduling_impl.FromGenericSchedulingImpl[source]

Bases: ProblemTransformation[GenericSchedulingImplProblem, GenericSchedulingImplSolution, SpecificSchedulingProblem, SpecificSchedulingSolution], Generic[SpecificSchedulingProblem, SpecificSchedulingSolution]

Transform the generic implementation of a scheduling problem into a specific one.

This is still an abstract class, as the transform_problem() remains to implement.

back_transform_solution(solution: SpecificSchedulingSolution, source_problem: GenericSchedulingImplProblem) → GenericSchedulingImplSolution[source]

Convert solution from target problem back to source problem.

This is REQUIRED for all transformations.

Parameters:

  • solution – Solution in target problem space

  • source_problem – Original problem (to associate with back-transformed solution)

Returns:

Corresponding solution in source problem space

class discrete_optimization.generic_tasks_tools.transformations.generic_scheduling_impl.ToGenericSchedulingImpl[source]

Bases: ProblemTransformation[SpecificSchedulingProblem, SpecificSchedulingSolution, GenericSchedulingImplProblem, GenericSchedulingImplSolution], Generic[SpecificSchedulingProblem, SpecificSchedulingSolution]

Transform a specific scheduling problem into the generic implementation.

This is still an abstract class, as convert_solution_from_raw_generic_to_specific() remains to implement.

back_transform_solution(solution: GenericSchedulingImplSolution, source_problem: SpecificSchedulingProblem) → SpecificSchedulingSolution[source]

Convert solution from target problem back to source problem.

This is REQUIRED for all transformations.

Parameters:

  • solution – Solution in target problem space

  • source_problem – Original problem (to associate with back-transformed solution)

Returns:

Corresponding solution in source problem space

forward_transform_solution(solution: SpecificSchedulingSolution, target_problem: GenericSchedulingImplProblem) → GenericSchedulingImplSolution | None[source]

Convert solution from source problem to target problem.

This is OPTIONAL - only needed for warmstart support. Return None if transformation not supported/meaningful.

Parameters:

  • solution – Solution in source problem space

  • target_problem – Transformed problem (to associate with forward-transformed solution)

Returns:

Corresponding solution in target problem space, or None if not supported

is_bidirectional(source_problem: SpecificSchedulingProblem) → bool[source]

Check if transformation supports both directions.

Parameters:

source_problem – Problem to check bidirectionality for

Returns:

True if forward_transform_solution is implemented

transform_objective(source_problem: SpecificSchedulingProblem) → tuple[Objective | Iterable[tuple[Objective, int]], Callable[[GenericSchedulingImplSolution], int] | None, dict[Hashable, int] | None, bool][source]

Transform scheduling problem objective for the generic implementation.

This default implementation returns default values of GenericSchedulingImplProblem. To be overriden in child classes.

Returns:

the corresponding arguments of GenericSchedulingImplProblem.__init__(), resulting to computing makespan + time penalty. See its documentation for more details.

Return type:

objective, custom_evaluate_fn, objective_resource_weights, compute_time_penalty

transform_problem(source_problem: SpecificSchedulingProblem) → GenericSchedulingImplProblem[source]

Transform source problem to target problem.

This method should be deterministic: same source → same target.

Parameters:

source_problem – The original problem to transform

Returns:

Transformed problem instance

abstractmethod transform_solution_from_raw_generic_to_specific(raw_sol: RawSolution[Hashable, Hashable, Hashable], source_problem: SpecificSchedulingProblem) → SpecificSchedulingSolution[source]

Convert a raw solution (from generic problem) into a specific solution to the source problem.

Parameters:

source_problem

Returns:

discrete_optimization.generic_tasks_tools.transformations.generic_scheduling_impl.convert_solution_from_specific_to_generic(solution: SpecificSchedulingSolution, generic_problem: GenericSchedulingImplProblem) → GenericSchedulingImplSolution[source]

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