discrete_optimization.jsp.solvers package

Submodules

discrete_optimization.jsp.solvers.cpsat module

class discrete_optimization.jsp.solvers.cpsat.CpSatJspSolver(problem: JobShopProblem, **kwargs: Any)

Bases: SchedulingCpSatSolver[tuple[int, int]], WarmstartMixin

get_makespan_upper_bound() → int

Get a upper bound on global makespan.

get_task_start_or_end_variable(task: tuple[int, int], start_or_end: StartOrEnd) → LinearExpr | IntVar | int | int8 | uint8 | int32 | uint32 | int64 | uint64

Retrieve the variable storing the start or end time of given task.

Parameters:

• task

• start_or_end

Returns:

init_model(**args: Any) → None

Init cp model and reset stored variables if any.

problem: JobShopProblem

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → JobShopSolution

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: JobShopSolution) → None

Make the solver warm start from the given solution.

discrete_optimization.jsp.solvers.dp module

class discrete_optimization.jsp.solvers.dp.DpJspSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

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

Initialize internal model used to solve.

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

problem: JobShopProblem

retrieve_solution(sol: Solution) → Solution

set_warm_start(solution: JobShopSolution) → None

Make the solver warm start from the given solution.

discrete_optimization.jsp.solvers.optal module

class discrete_optimization.jsp.solvers.optal.OptalJspSolver(problem: JobShopProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: OptalSolver

build_command(parameters_cp: ParametersCp | None = None, time_limit: int = 10, **args: Any)

Build the command line call for optal cp. You can pass parameters from the Parameters class of optal cp for example : searchType=fds, worker0-1.noOverlapPropagationLevel=4 if you want worker 0 and 1 to use this parameters etc. TODO : list such parameters in hyperparameter of this wrapped solver.

init_model(**args: Any) → None

Instantiate a CP model instance

Afterwards, self.instance should not be None anymore.

problem: JobShopProblem

retrieve_current_solution(dict_results: dict) → Solution

discrete_optimization.jsp.solvers.optal.from_jsp_to_jsplib(problem: JobShopProblem) → str

discrete_optimization.jsp.solvers.tempo module

class discrete_optimization.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)

Bases: TempoSchedulingSolver

init_model(**kwargs: Any) → None

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

discrete_optimization.jsp.solvers.tempo.from_jsp_to_jsplib(problem: JobShopProblem) → str

discrete_optimization.jsp.solvers.tempo.parse_output(solver_output: str, problem: JobShopProblem) → JobShopSolution

Module contents