discrete_optimization.generic_tasks_tools.solvers.optalcp package

Submodules

discrete_optimization.generic_tasks_tools.solvers.optalcp.allocation module

class discrete_optimization.generic_tasks_tools.solvers.optalcp.allocation.AllocationOptalSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: OptalCpSolver, AllocationCpSolver[Task, UnaryResource]

Base class for allocation cp-sat solvers using a binary modelling. I.e. using 0-1 variables to model allocation status of each couple (task, unary_resource) This is a more general modelisation thant the integer one as it allows allocation of multiple resources.

add_allocation_changes_constraints(ref: AllocationSolution[Task, UnaryResource]) → list[Any]

Add and return constraints so that change variables reflect diff to ref.

add_constraint_nb_unary_resource_usages(sign: SignEnum, target: int, tasks: Iterable[Task] | None = None, unary_resources: Iterable[UnaryResource] | None = None) → list[Any]

add_constraint_on_nb_allocation_changes(ref: AllocationSolution[Task, UnaryResource], nb_changes: int, sign: SignEnum = SignEnum.LEQ) → list[Any]

Add contraint on maximal number of allocation changes from the given reference.

Parameters:

• ref

• nb_changes – maximal number of changes

Returns:

resulting constraints

add_constraint_on_task_unary_resource_allocation(task: Task, unary_resource: UnaryResource, used: bool) → list[Any]

Add constraint on allocation of given unary resource for the given task

Parameters:

• task

• unary_resource

• used – if True, we enforce the allocation of unary_resource to task, else we prevent it

Returns:

resulting constraints

add_constraint_on_total_nb_usages(sign: SignEnum, target: int) → list[Any]

add_constraint_on_unary_resource_nb_usages(unary_resource: UnaryResource, sign: SignEnum, target: int) → list[Any]

allocation_changes_variables: dict[tuple[Task, UnaryResource], 'cp.IntExpr']

Variables tracking allocation changes from a given reference.

allocation_changes_variables_created = False

Flag telling whether ‘allocation changes variables’ have been created

at_most_one_unary_resource_per_task = False

Flag telling if the problem accept at most one unary_resource per task.

Default to False, ie several resources allowed per task.

create_allocation_changes_variables()

Create variables necessary for constraint on nb of changes.

create_done_variables()

create_used_variables()

done_variables: dict[Task, cp.BoolExpr]

Variables tracking whether a task has at least one unary resource allocated.

done_variables_created = False

Flag telling whether ‘done variables’ have been created

get_default_tasks_n_unary_resources(tasks: Iterable[Task] | None = None, unary_resources: Iterable[UnaryResource] | None = None) → tuple[Iterable[Task], Iterable[UnaryResource]]

get_nb_tasks_done_variable() → Any

Construct and get the variable tracking number of tasks with at least a resource allocated.

Returns:

objective variable to minimize

get_nb_unary_resources_used_variable() → Any

Construct and get the variable tracking number of tasks with at least a resource allocated.

Returns:

objective variable to minimize

abstractmethod get_task_unary_resource_is_present_variable(task: Task, unary_resource: UnaryResource) → cp.BoolExpr

Return a 0-1 variable/expression telling if the unary_resource is used for the task.

NB: sometimes the given resource is never to be used by a task and the variable has not been created. The convention is to return 0 in that case.

init_model(**kwargs: Any) → None

Init cp model and reset stored variables if any.

property subset_tasks_of_interest: Iterable[Task]

Subset of tasks of interest used for the objective.

By default, all tasks.

property subset_unaryresources_allowed: Iterable[UnaryResource]

Unary resources allowed to solve the problem.

By default, all unary resources.

used_variables: dict[UnaryResource, cp.BoolVar]

Variables tracking whether a unary resource has been used at least once.

used_variables_created = False

Flag telling whether ‘used variables’ have been created

discrete_optimization.generic_tasks_tools.solvers.optalcp.multimode module

class discrete_optimization.generic_tasks_tools.solvers.optalcp.multimode.MultimodeOptalSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: OptalCpSolver, MultimodeCpSolver[Task]

add_constraint_on_task_mode(task: Task, mode: int) → list[Any]

Add constraint on task mode

The mode of task is fixed to mode.

Parameters:

• task

• mode

Returns:

resulting constraints

abstractmethod get_task_mode_is_present_variable(task: Task, mode: int) → cp.BoolExpr

Retrieve the 0-1 variable/expression telling if the mode is used for the task.

Parameters:

• task

• mode

Returns:

discrete_optimization.generic_tasks_tools.solvers.optalcp.scheduling module

class discrete_optimization.generic_tasks_tools.solvers.optalcp.scheduling.SchedulingOptalSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: OptalCpSolver, SchedulingCpSolver[Task]

add_constraint_chaining_tasks(task1: Task, task2: Task) → list[Any]

Add constraint chaining task1 with task2

task2 start == task1 end

Parameters:

• task1

• task2

Returns:

resulting constraints

add_constraint_on_task(task: Task, start_or_end: StartOrEnd, sign: SignEnum, time: int) → list['cp.BoolExpr']

Add constraint on given task start or end

task start or end must compare to time according to sign

Parameters:

• task

• start_or_end

• sign

• time

Returns:

resulting constraints

get_subtasks_makespan_variable(subtasks: Iterable[Task]) → Any

Construct and get the variable tracking the makespan on a subset of tasks.

Beware: a further call to get_subtasks_makespan_variable with another subset of tasks can change the constraints on this variable and thus make it obsolete.

Parameters:

subtasks

Returns:

objective variable to minimize

get_subtasks_sum_end_time_variable(subtasks: Iterable[Task]) → Any

Construct and get the variable tracking the sum of end times on a subset of tasks.

Parameters:

subtasks

Returns:

objective variable to minimize

get_subtasks_sum_start_time_variable(subtasks: Iterable[Task]) → Any

Construct and get the variable tracking the sum of start times on a subset of tasks.

Parameters:

subtasks

Returns:

objective variable to minimize

abstractmethod get_task_interval_variable(task: Task) → cp.IntervalVar

Retrieve the interval variable of given task.

get_task_start_or_end_variable(task: Task, start_or_end: StartOrEnd) → cp.IntExpr

Retrieve the variable storing the start or end time of given task.

Parameters:

• task

• start_or_end

Returns:

Module contents