discrete_optimization.jsp package

Subpackages

• discrete_optimization.jsp.solvers package
  • Submodules
  • discrete_optimization.jsp.solvers.cpsat module
    • CpSatJspSolver
      • CpSatJspSolver.init_model()
      • CpSatJspSolver.problem
      • CpSatJspSolver.retrieve_solution()
      • CpSatJspSolver.set_warm_start()
  • discrete_optimization.jsp.solvers.dp module
    • DpJspSolver
      • DpJspSolver.hyperparameters
      • DpJspSolver.init_model()
      • DpJspSolver.problem
      • DpJspSolver.retrieve_solution()
      • DpJspSolver.set_warm_start()
  • Module contents

Submodules

discrete_optimization.jsp.parser module

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

Get datasets available for jobshop.

Params:

data_folder: folder where datasets for jobshop whould be find.

If None, we look in “jobshop” subdirectory of data_home.

data_home: root directory for all datasets. Is None, set by

default to “~/discrete_optimization_data “

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

discrete_optimization.jsp.problem module

class discrete_optimization.jsp.problem.JobShopProblem(list_jobs: list[list[Subjob]], n_jobs: int | None = None, n_machines: int | None = None, horizon: int | None = None)

Bases: Problem

evaluate(variable: JobShopSolution) → 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[list[Subjob]]

n_jobs: int

n_machines: int

satisfy(variable: JobShopSolution) → 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.jsp.problem.JobShopSolution(problem: JobShopProblem, schedule: list[list[tuple[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.jsp.problem.Subjob(machine_id: int, processing_time: int)

Bases: object

machine_id: int

processing_time: int

discrete_optimization.jsp.utils module

discrete_optimization.jsp.utils.transform_jsp_to_rcpsp(jsp_problem: JobShopProblem) → RcpspProblem

Module contents