discrete_optimization.shop.fjsp.solvers package

Submodules

discrete_optimization.shop.fjsp.solvers.cpsat module

discrete_optimization.shop.fjsp.solvers.dp module

class discrete_optimization.shop.fjsp.solvers.dp.DpFjspSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)[source]

Bases: DpSolver, WarmstartMixin

hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='solver', default=<class 'builtins.CABS'>, depends_on=None, name_in_kwargs='solver'), CategoricalHyperparameter(name='add_penalty_on_inefficiency', default=True, depends_on=None, name_in_kwargs='add_penalty_on_inefficiency')]

Hyperparameters available for this solver.

These hyperparameters are to be feed to **kwargs found in

  • __init__()

  • init_model() (when available)

  • solve()

init_model(**kwargs: Any) → None[source]

Initialize internal model used to solve.

Can initialize a ortools, milp, gurobi, … model.

problem: FJobShopProblem
retrieve_solution(sol: Solution) → Solution[source]
set_warm_start(solution: AnyShopSolution) → None[source]

Make the solver warm start from the given solution.

discrete_optimization.shop.fjsp.solvers.lns_cpsat module

class discrete_optimization.shop.fjsp.solvers.lns_cpsat.FjspConstraintHandler(problem: FJobShopProblem, fraction_segment_to_fix: float = 0.9)[source]

Bases: OrtoolsCpSatConstraintHandler

adding_constraint_from_results_store(solver: CpSatShopSolver, result_storage: ResultStorage, result_storage_last_iteration: ResultStorage, **kwargs: Any) → Iterable[Constraint][source]

Add constraints to the internal model of a solver based on previous solutions

Parameters:

  • solver – solver whose internal model is updated

  • result_storage – all results so far

  • result_storage_last_iteration – results from last LNS iteration only

  • **kwargs

Returns:

list of added constraints

class discrete_optimization.shop.fjsp.solvers.lns_cpsat.NeighFjspConstraintHandler(problem: FJobShopProblem, neighbor_builder: NeighborBuilderSubPart)[source]

Bases: OrtoolsCpSatConstraintHandler

adding_constraint_from_results_store(solver: CpSatShopSolver, result_storage: ResultStorage, result_storage_last_iteration: ResultStorage, **kwargs: Any) → Iterable[Constraint][source]

Add constraints to the internal model of a solver based on previous solutions

Parameters:

  • solver – solver whose internal model is updated

  • result_storage – all results so far

  • result_storage_last_iteration – results from last LNS iteration only

  • **kwargs

Returns:

list of added constraints

class discrete_optimization.shop.fjsp.solvers.lns_cpsat.NeighborBuilderSubPart(problem: FJobShopProblem, nb_cut_part: int = 10)[source]

Bases: object

Cut the schedule in different subpart in the increasing order of the schedule.

find_subtasks(current_solution: AnyShopSolution, subtasks: set[Hashable] | None = None) → tuple[set[tuple[int, int]], set[tuple[int, int]]][source]

discrete_optimization.shop.fjsp.solvers.optal module

class discrete_optimization.shop.fjsp.solvers.optal.OptalFJspSolver(problem: FJobShopProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)[source]

Bases: SchedulingOptalSolver[tuple[int, int]]

get_task_interval_variable(task: Task) → cp.IntervalVar[source]

Retrieve the interval variable of given task.

init_model(**args: Any) → None[source]

Instantiate a CP model instance

Afterwards, self.instance should not be None anymore.

problem: FJobShopProblem
retrieve_solution(result: cp.SolveResult) → Solution[source]

Return a d-o solution from the variables computed by minizinc.

Parameters:

result – output of the cp.solve

Returns:

Module contents