discrete_optimization.singlemachine.solvers package

Submodules

discrete_optimization.singlemachine.solvers.cpsat module

class discrete_optimization.singlemachine.solvers.cpsat.CpsatWTSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)[source]

Bases: OrtoolsCpSatSolver

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

Instantiate a CP model instance

Afterwards, self.instance should not be None anymore.

problem: WeightedTardinessProblem
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.

variables: dict

discrete_optimization.singlemachine.solvers.dp module

class discrete_optimization.singlemachine.solvers.dp.DpWTSolver(problem: WeightedTardinessProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)[source]

Bases: DpSolver, WarmstartMixin

hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='add_dominated_transition', default=False, depends_on=None, name_in_kwargs='add_dominated_transition')]

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: WeightedTardinessProblem
retrieve_solution(sol: Solution) → Solution[source]
set_warm_start(solution: WTSolution) → None[source]

Make the solver warm start from the given solution.

Module contents