discrete_optimization.fjsp.solvers package

Submodules

discrete_optimization.fjsp.solvers.cpsat module

class discrete_optimization.fjsp.solvers.cpsat.CpSatFjspSolver(problem: FJobShopProblem, **kwargs: Any)[source]

Bases: OrtoolsCpSatSolver, WarmstartMixin

create_disjunctive_constraints(**args)[source]
create_is_present_constraints()[source]
create_precedence_constraints()[source]
create_vars(**args)[source]
hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='duplicate_temporal_var', default=False, depends_on=None, name_in_kwargs='duplicate_temporal_var'), CategoricalHyperparameter(name='add_cumulative_constraint', default=False, depends_on=None, name_in_kwargs='add_cumulative_constraint')]

Hyperparameters available for this solver.

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

  • __init__()

  • init_model() (when available)

  • solve()

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

Instantiate a CP model instance

Afterwards, self.instance should not be None anymore.

problem: FJobShopProblem
retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → Solution[source]

Construct a do solution from the cpsat solver internal solution.

It will be called each time the cpsat solver find a new solution. At that point, value of internal variables are accessible via cpsolvercb.Value(VARIABLE_NAME).

Parameters:

cpsolvercb – the ortools callback called when the cpsat solver finds a new solution.

Returns:

the intermediate solution, at do format.

set_warm_start(solution: FJobShopSolution) → None[source]

Make the solver warm start from the given solution.

discrete_optimization.fjsp.solvers.dp module

class discrete_optimization.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: FJobShopSolution) → None[source]

Make the solver warm start from the given solution.

discrete_optimization.fjsp.solvers.lns_cpsat module

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

Bases: OrtoolsCpSatConstraintHandler

adding_constraint_from_results_store(solver: CpSatFjspSolver, result_storage: ResultStorage, **kwargs: Any) → Iterable[Constraint][source]
class discrete_optimization.fjsp.solvers.lns_cpsat.NeighFjspConstraintHandler(problem: FJobShopProblem, neighbor_builder: NeighborBuilderSubPart)[source]

Bases: OrtoolsCpSatConstraintHandler

adding_constraint_from_results_store(solver: CpSatFjspSolver, result_storage: ResultStorage, **kwargs: Any) → Iterable[Constraint][source]
class discrete_optimization.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: FJobShopSolution, subtasks: set[Hashable] | None = None) → tuple[set[tuple[int, int]], set[tuple[int, int]]][source]

Module contents