discrete_optimization.fjsp package

Subpackages

• discrete_optimization.fjsp.solvers package
  • Submodules
  • discrete_optimization.fjsp.solvers.cpsat module
    • CpSatFjspSolver
      • CpSatFjspSolver.create_disjunctive_constraints()
      • CpSatFjspSolver.create_is_present_constraints()
      • CpSatFjspSolver.create_precedence_constraints()
      • CpSatFjspSolver.create_vars()
      • CpSatFjspSolver.get_makespan_upper_bound()
      • CpSatFjspSolver.get_task_mode_is_present_variable()
      • CpSatFjspSolver.get_task_start_or_end_variable()
      • CpSatFjspSolver.hyperparameters
      • CpSatFjspSolver.init_model()
      • CpSatFjspSolver.problem
      • CpSatFjspSolver.retrieve_solution()
      • CpSatFjspSolver.set_warm_start()
  • discrete_optimization.fjsp.solvers.dp module
    • DpFjspSolver
      • DpFjspSolver.hyperparameters
      • DpFjspSolver.init_model()
      • DpFjspSolver.problem
      • DpFjspSolver.retrieve_solution()
      • DpFjspSolver.set_warm_start()
  • discrete_optimization.fjsp.solvers.lns_cpsat module
    • FjspConstraintHandler
      • FjspConstraintHandler.adding_constraint_from_results_store()
    • NeighFjspConstraintHandler
      • NeighFjspConstraintHandler.adding_constraint_from_results_store()
    • NeighborBuilderSubPart
      • NeighborBuilderSubPart.find_subtasks()
  • discrete_optimization.fjsp.solvers.optal module
    • OptalFJspSolver
      • OptalFJspSolver.build_command()
      • OptalFJspSolver.init_model()
      • OptalFJspSolver.problem
      • OptalFJspSolver.retrieve_current_solution()
    • deparse_file()
  • Module contents

Submodules

discrete_optimization.fjsp.parser module

discrete_optimization.fjsp.parser.get_data_available(data_folder: str | None = None, data_home: str | None = None) → list[str]

Get datasets available for jobshop.

discrete_optimization.fjsp.parser.parse_file(file_path: str)

discrete_optimization.fjsp.problem module

class discrete_optimization.fjsp.problem.FJobShopProblem(list_jobs: list[Job], n_jobs: int | None = None, n_machines: int | None = None, horizon: int | None = None)

Bases: SchedulingProblem[tuple[int, int]], MultimodeProblem[tuple[int, int]], PrecedenceProblem[tuple[int, int]]

evaluate(variable: FJobShopSolution) → dict[str, float]

Evaluate a given solution object for the given problem.

This method should return a dictionnary of KPI, that can be then used for mono or multiobjective optimization.

Parameters:

variable (Solution) – the Solution object to evaluate.

Returns: dictionnary of float kpi for the solution.

get_attribute_register() → EncodingRegister

Returns how the Solution should be encoded.

Returns (EncodingRegister): content of the encoding of the solution

get_last_tasks() → list[tuple[int, int]]

Get a sublist of tasks that are candidate to be the last one scheduled.

Default to all tasks.

get_makespan_upper_bound() → int

Get a upper bound on global makespan.

get_objective_register() → ObjectiveRegister

Returns the objective definition.

Returns (ObjectiveRegister): object defining the objective criteria.

get_precedence_constraints() → dict[tuple[int, int], list[tuple[int, int]]]

Map each task to the tasks that need to be performed after it.

get_solution_type() → type[Solution]

Returns the class implementation of a Solution.

Returns (class): class object of the given Problem.

get_task_modes(task: tuple[int, int]) → set[int]

Retrieve mode found for given task.

Parameters:

task

Returns:

list_jobs: list[Job]

n_jobs: int

n_machines: int

satisfy(variable: FJobShopSolution) → bool

Computes if a solution satisfies or not the constraints of the problem.

Parameters:

variable – the Solution object to check satisfability

Returns (bool): boolean true if the constraints are fulfilled, false elsewhere.

property tasks_list: list[tuple[int, int]]

class discrete_optimization.fjsp.problem.FJobShopSolution(problem: FJobShopProblem, schedule: list[list[tuple[int, int, int, int]]])

Bases: SchedulingSolution[tuple[int, int]], MultimodeSolution[tuple[int, int]]

change_problem(new_problem: FJobShopProblem) → None

If relevant to the optimisation problem, change the underlying problem instance for the solution.

This method can be used to evaluate a solution for different instance of problems.

Parameters:

new_problem (Problem) – another problem instance from which the solution can be evaluated

Returns: None

copy() → FJobShopSolution

Deep copy of the solution.

The copy() function should return a new object containing the same input as the current object, that respects the following expected behaviour: -y = x.copy() -if do some inplace change of y, the changes are not done in x.

Returns: a new object from which you can manipulate attributes without changing the original object.

get_end_time(task: tuple[int, int]) → int

get_machine(task: tuple[int, int]) → int

get_mode(task: tuple[int, int]) → int

Get ‘mode’ of given task, aka chosen machine.

get_start_time(task: tuple[int, int]) → int

problem: FJobShopProblem

class discrete_optimization.fjsp.problem.Job(job_id: 'int', sub_jobs: 'list[SubjobOptions]')

Bases: object

job_id: int

sub_jobs: list[list[Subjob]]

Module contents