discrete_optimization.shop.jsp.solvers package

Submodules

discrete_optimization.shop.jsp.solvers.cpsat module

discrete_optimization.shop.jsp.solvers.dp module

class discrete_optimization.shop.jsp.solvers.dp.DpJspSolver(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')]

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: JobShopProblem
retrieve_solution(sol: Solution) → Solution[source]
set_warm_start(solution: JobShopSolution) → None[source]

Make the solver warm start from the given solution.

transitions: dict

discrete_optimization.shop.jsp.solvers.optal module

class discrete_optimization.shop.jsp.solvers.optal.OptalJspSolver(problem: JobShopProblem, 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(**kwargs)[source]

Builds the OptalCP model for the JSP problem.

problem: JobShopProblem
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:

discrete_optimization.shop.jsp.solvers.tempo module

class discrete_optimization.shop.jsp.solvers.tempo.TempoJspScheduler(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, input_format: FormatEnum | None = None, path_to_tempo_executable: str | None = None, **kwargs: Any)[source]

Bases: TempoSchedulingSolver

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

For tempo solver, this should transform the python object into some format that tempo can understand. For now it’s via the creation of a temporary _file_input.

problem: JobShopProblem
retrieve_solution(path_to_output: str, process_stdout: str) → JobShopSolution[source]
discrete_optimization.shop.jsp.solvers.tempo.from_jsp_to_jsplib(problem: JobShopProblem) → str[source]
discrete_optimization.shop.jsp.solvers.tempo.parse_output(solver_output: str, problem: JobShopProblem) → JobShopSolution[source]

Module contents