discrete_optimization.shop package

Subpackages

Submodules

discrete_optimization.shop.base module

class discrete_optimization.shop.base.AnyShopSolution(problem: CommonShopProblem, schedule: list[list[tuple[int, int]]], machine_index: list[list[int]] = None, recipe_index: list[list[int]] = None)[source]

Bases: GenericSchedulingSolution[tuple[int, int], None, None, int, None], WithoutSkillSolution[tuple[int, int], None, int, None], WithoutNonRenewableResourceSolution[tuple[int, int]], WithoutAllocationSolution[tuple[int, int]]

copy() → AnyShopSolution[source]

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

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

get_start_time(task: tuple[int, int]) → int[source]
machine_index: list[list[int]]
problem: CommonShopProblem
recipe_index: list[list[int]]
schedule: list[list[tuple[int, int]]]
class discrete_optimization.shop.base.CommonShopProblem(list_jobs: list[Job], n_jobs: int = None, n_machines: int = None, horizon: int = None)[source]

Bases: GenericSchedulingProblem[tuple[int, int], None, None, int, None], WithoutSkillProblem[tuple[int, int], None, int, None], WithoutNonRenewableResourceProblem[tuple[int, int]], WithoutAllocationProblem[tuple[int, int]]

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

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_cumulative_resource_consumption(resource: int, task: tuple[int, int], mode: int) → int[source]

Get cumulative resource consumption of the task in the given mode

Parameters:

  • resource – cumulative resource

  • task

  • mode – not used for single mode problems

Returns:

the consumption for cumulative resources.

get_makespan_upper_bound() → int[source]

Get an upper bound on global makespan.

get_no_overlap() → set[frozenset[tuple[int, int]]][source]

An object in this returned set is a (frozen) set of task, where no task should overlap with another one in this set

get_objective_register() → ObjectiveRegister[source]

Returns the objective definition.

Returns (ObjectiveRegister): object defining the objective criteria.

get_solution_type() → type[Solution][source]

Returns the class implementation of a Solution.

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

get_task_mode_duration(task: tuple[int, int], mode: int) → int[source]

Get task duration according to mode.

Parameters:

  • task

  • mode – not used for single-mode problems

Returns:

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

Retrieve mode found for given task.

Parameters:

task

Returns:

list_jobs: list[Job]
machine_to_mode_mapping(task: tuple[int, int])[source]
mode_to_machine_mapping(task: tuple[int, int])[source]
n_jobs: int
n_machines: int
property non_skill_cumulative_resources_list: list[int]

List of cumulative resources that are not skills.

satisfy(variable: AnyShopSolution) → bool[source]

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]]

List of all tasks to schedule or allocate to.

class discrete_optimization.shop.base.Job(job_index: int, subjobs: list[Subjob])[source]

Bases: object

job_index: int
subjobs: list[Subjob]
class discrete_optimization.shop.base.Subjob(subjob_index: int, job_index: int, recipes: list[SubjobRecipe])[source]

Bases: object

job_index: int
recipes: list[SubjobRecipe]
subjob_index: int
class discrete_optimization.shop.base.SubjobRecipe(machine_index: int, processing_time: int)[source]

Bases: object

machine_index: int
processing_time: int
discrete_optimization.shop.base.Task

(job index, subjob index).

Type:

Task representation

alias of tuple[int, int]

discrete_optimization.shop.utils module

discrete_optimization.shop.utils.plot_shop_solution(solution: AnyShopSolution, title: str = 'Job Shop Schedule')[source]

Creates a Gantt chart visualization for a JobShopSolution.

Parameters:

  • solution – A JobShopSolution object containing the problem and schedule.

  • title – The title for the plot.

discrete_optimization.shop.utils.transform_shop_to_rcpsp(problem: CommonShopProblem) → RcpspProblem[source]

Module contents