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.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()
  • 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: Problem

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_objective_register() → ObjectiveRegister

Returns the objective definition.

Returns (ObjectiveRegister): object defining the objective criteria.

get_solution_type() → type[Solution]

Returns the class implementation of a Solution.

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

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.

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

Bases: Solution

change_problem(new_problem: Problem) → 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() → Solution

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.

class discrete_optimization.fjsp.problem.Job(job_id: int, sub_jobs: List[list[discrete_optimization.jsp.problem.Subjob]])

Bases: object

job_id: int

sub_jobs: List[list[Subjob]]

Module contents