discrete_optimization.singlemachine package

Subpackages

• discrete_optimization.singlemachine.solvers package
  • Submodules
  • discrete_optimization.singlemachine.solvers.cpmpy_solver module
    • CpmpySingleMachineSolver
      • CpmpySingleMachineSolver.get_hard_meta_constraints()
      • CpmpySingleMachineSolver.get_soft_meta_constraints()
      • CpmpySingleMachineSolver.init_model()
      • CpmpySingleMachineSolver.retrieve_current_solution()
    • SingleMachineModel
      • SingleMachineModel.CP
      • SingleMachineModel.LP
  • discrete_optimization.singlemachine.solvers.cpsat module
    • CpsatWTSolver
      • CpsatWTSolver.get_task_start_or_end_variable()
      • CpsatWTSolver.init_model()
      • CpsatWTSolver.problem
      • CpsatWTSolver.retrieve_solution()
      • CpsatWTSolver.set_warm_start()
      • CpsatWTSolver.variables
  • discrete_optimization.singlemachine.solvers.dp module
    • DpWTSolver
      • DpWTSolver.hyperparameters
      • DpWTSolver.init_model()
      • DpWTSolver.problem
      • DpWTSolver.retrieve_solution()
      • DpWTSolver.set_warm_start()
  • discrete_optimization.singlemachine.solvers.greedy module
    • GreedySingleMachineSolver
      • GreedySingleMachineSolver.problem
      • GreedySingleMachineSolver.solve()
    • GreedySingleMachineWSPT
      • GreedySingleMachineWSPT.problem
      • GreedySingleMachineWSPT.solve()
  • discrete_optimization.singlemachine.solvers.lp module
    • GurobiSingleMachineSolver
      • GurobiSingleMachineSolver.convert_to_variable_values()
    • MathOptSingleMachineSolver
      • MathOptSingleMachineSolver.convert_to_variable_values()
  • discrete_optimization.singlemachine.solvers.optal module
    • OptalSingleMachineSolver
      • OptalSingleMachineSolver.build_command()
      • OptalSingleMachineSolver.init_model()
      • OptalSingleMachineSolver.problem
      • OptalSingleMachineSolver.retrieve_current_solution()
    • to_dict()
  • Module contents

Submodules

discrete_optimization.singlemachine.parser module

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

Get datasets available for tsp.

Params:

data_folder: folder where datasets for weighted tardiness problem should be found.

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

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

default to “~/discrete_optimization_data “

discrete_optimization.singlemachine.parser.parse_file(path: str, num_jobs: int = None)

discrete_optimization.singlemachine.parser.parse_wt_content(file_content: str, num_jobs: int) → list[WeightedTardinessProblem]

Parses a weighted tardiness file with a known number of jobs per instance.

Parameters:

• file_content (str) – The full content of the text file.

• num_jobs (int) – The number of jobs per instance (e.g., 40 for wt40.txt).

Returns:

A list of parsed problem instances.

Return type:

List[WeightedTardinessProblem]

discrete_optimization.singlemachine.problem module

class discrete_optimization.singlemachine.problem.WTSolution(problem: WeightedTardinessProblem, schedule: list[tuple[int, int]] = None, permutation: list[int] = None)

Bases: SchedulingSolution[int]

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.

get_end_time(task: int) → int

get_start_time(task: int) → int

lazy_copy() → Solution

This function should return a new object but possibly with mutable attributes from the original objects.

A typical use of lazy copy is in evolutionary algorithms or genetic algorithm where the use of local move don’t need to do a possibly costly deepcopy.

Returns (Solution): copy (possibly shallow) of the Solution

class discrete_optimization.singlemachine.problem.WeightedTardinessProblem(num_jobs: int, processing_times: List[int], weights: List[int], due_dates: List[int], release_dates: List[int] | None = None)

Bases: SchedulingProblem[int]

Represents a single instance of the single-machine weighted tardiness problem.

evaluate(variable: WTSolution) → 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.

evaluate_from_encoding(permutation: list[int], encoding_name)

get_attribute_register() → EncodingRegister

Returns how the Solution should be encoded.

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

get_dummy_solution()

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_solution_type() → type[Solution]

Returns the class implementation of a Solution.

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

satisfy(variable: WTSolution) → 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.

Module contents