discrete_optimization.rcpsp.solvers.preemptive package

Submodules

discrete_optimization.rcpsp.solvers.preemptive.cpsat module

class discrete_optimization.rcpsp.solvers.preemptive.cpsat.CpSatCalendarPreemptiveSolver(problem: PreemptiveRcpspProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs)

Bases: OrtoolsCpSatSolver

constraint_duration_of_task(task_index: int, mode: int, duration_per_interval: dict[int, list[tuple[int, int]]])

constraint_duration_of_tasks()

Tricky constraint : should take into account the partial preemption possibility, which makes duration variable based on calendars

constraint_precedence()

constraint_resource()

constraint_resource_cumulative(resource: str, fake_tasks: list[dict[str, int]])

constraint_resource_non_renewable(resource: str)

create_main_variables()

init_model(**kwargs: Any) → None

Init cp model and reset stored variables if any.

problem: PreemptiveRcpspProblem

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → Solution

Construct a do solution from the cpsat solver internal solution.

It will be called each time the cpsat solver find a new solution. At that point, value of internal variables are accessible via cpsolvercb.Value(VARIABLE_NAME).

Parameters:

cpsolvercb – the ortools callback called when the cpsat solver finds a new solution.

Returns:

the intermediate solution, at do format.

class discrete_optimization.rcpsp.solvers.preemptive.cpsat.CpSatPreemptiveRcpspSolver(problem: PreemptiveRcpspProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs)

Bases: OrtoolsCpSatSolver

add_lexico_constraint(obj: str, value: float) → Iterable[Any]

Add a constraint on a computed sub-objective

Parameters:

• obj – a string representing the desired objective. Should be one of self.get_lexico_objectives_available().

• value – the limiting value. If the optimization direction is maximizing, this is a lower bound, else this is an upper bound.

Returns:

the created constraints.

constraint_convention_variables()

constraint_precedence()

constraint_resource()

constraint_resource_cumulative(resource: str, fake_tasks: list[dict[str, int]])

constraint_resource_non_renewable(resource: str)

constraint_variable_to_duration()

create_duration_variables()

create_modes_variables()

create_preempt_variables(max_nb_preemption: int | None = None) → None

create_resource_consumption_variables()

get_lexico_objective_value(obj: str, res: ResultStorage) → float

Get best internal model objective value found by last call to solve().

The default implementation consists in using the fit of the last solution in result_storage. This assumes: - that the last solution is the best one for the objective considered - that no aggregation was performed but rather that the fitness is a TupleFitness

with values in the same order as self.problem.get_objective_names().

Parameters:

• obj – a string representing the desired objective. Should be one of self.get_lexico_objectives_available().

• res – result storage returned by last call to solve().

Returns:

get_lexico_objectives_available() → list[str]

List objectives available for lexico optimization

It corresponds to the labels accepted for obj argument for - set_lexico_objective() - add_lexico_constraint() - get_lexico_objective_value()

Default to self.problem.get_objective_names().

Returns:

implements_lexico_api() → bool

Tell whether this solver is implementing the api for lexicographic optimization.

Should return True only if

• set_lexico_objective()

• add_lexico_constraint()

• get_lexico_objective_value()

have been really implemented, i.e. - calling set_lexico_objective() and add_lexico_constraint()

should actually change the next call to solve(),

• get_lexico_objective_value() should correspond to the internal model objective

init_model(**kwargs: Any) → None

Init cp model and reset stored variables if any.

problem: PreemptiveRcpspProblem

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → PreemptiveRcpspSolution

Construct a do solution from the cpsat solver internal solution.

It will be called each time the cpsat solver find a new solution. At that point, value of internal variables are accessible via cpsolvercb.Value(VARIABLE_NAME).

Parameters:

cpsolvercb – the ortools callback called when the cpsat solver finds a new solution.

Returns:

the intermediate solution, at do format.

set_lexico_objective(obj: str) → None

Update internal model objective.

Parameters:

obj – a string representing the desired objective. Should be one of self.get_lexico_objectives_available().

Returns:

discrete_optimization.rcpsp.solvers.preemptive.cpsat.compute_binary_calendar_per_tasks(problem: PreemptiveRcpspProblem) → tuple[tuple, dict[tuple, ndarray], dict[tuple[int, int], tuple]]

discrete_optimization.rcpsp.solvers.preemptive.cpsat.compute_duration_function_time_cluster(orig_duration: int, resource_calendar: ndarray, cumulative_resource_calendar: ndarray)

discrete_optimization.rcpsp.solvers.preemptive.cpsat.transform_calendar_preemptive_solution_to_preemptive(solution: PreemptiveRcpspSolution, problem: PreemptiveRcpspProblem, resource_calendar_dict: dict[tuple, ndarray] = None, task_mode_to_calendar: dict[tuple, ndarray] = None) → PreemptiveRcpspSolution

discrete_optimization.rcpsp.solvers.preemptive.optal module

class discrete_optimization.rcpsp.solvers.preemptive.optal.OptalCalendarPreemptiveRcpspSolver(problem: PreemptiveRcpspProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs)

Bases: OptalCpSolver

constraint_duration_integral()

constraint_resource()

constraint_resource_cumulative(resource: str, fake_tasks: list[dict[str, int]])

constraint_resource_non_renewable(resource: str)

create_calendar_step_function()

create_main_variables()

create_precedence()

init_model(**kwargs: Any) → None

Instantiate a CP model instance

Afterwards, self.instance should not be None anymore.

problem: PreemptiveRcpspProblem

retrieve_solution(result: cp.SolveResult) → PreemptiveRcpspSolution

Return a d-o solution from the variables computed by minizinc.

Parameters:

result – output of the cp.solve

Returns:

class discrete_optimization.rcpsp.solvers.preemptive.optal.OptalPreemptiveRcpspSolver(problem: PreemptiveRcpspProblem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs)

Bases: OptalCpSolver

add_lexico_constraint(obj: str, value: float) → Iterable[Any]

Add a constraint on a computed sub-objective

Parameters:

• obj – a string representing the desired objective. Should be one of self.get_lexico_objectives_available().

• value – the limiting value. If the optimization direction is maximizing, this is a lower bound, else this is an upper bound.

Returns:

the created constraints.

constraint_convention_variables()

constraint_precedence()

constraint_resource()

constraint_resource_cumulative(resource: str, fake_tasks: list[dict[str, int]])

constraint_resource_non_renewable(resource: str)

constraint_variable_to_duration()

create_duration_variables()

create_modes_variables()

create_preempt_variables(max_nb_preemption: int | None = None) → None

create_resource_consumption_variables()

get_lexico_objective_value(obj: str, res: ResultStorage) → float

Get best internal model objective value found by last call to solve().

The default implementation consists in using the fit of the last solution in result_storage. This assumes: - that the last solution is the best one for the objective considered - that no aggregation was performed but rather that the fitness is a TupleFitness

with values in the same order as self.problem.get_objective_names().

Parameters:

• obj – a string representing the desired objective. Should be one of self.get_lexico_objectives_available().

• res – result storage returned by last call to solve().

Returns:

get_lexico_objectives_available() → list[str]

List objectives available for lexico optimization

It corresponds to the labels accepted for obj argument for - set_lexico_objective() - add_lexico_constraint() - get_lexico_objective_value()

Default to self.problem.get_objective_names().

Returns:

implements_lexico_api() → bool

Tell whether this solver is implementing the api for lexicographic optimization.

Should return True only if

• set_lexico_objective()

• add_lexico_constraint()

• get_lexico_objective_value()

have been really implemented, i.e. - calling set_lexico_objective() and add_lexico_constraint()

should actually change the next call to solve(),

• get_lexico_objective_value() should correspond to the internal model objective

init_model(**kwargs: Any) → None

Instantiate a CP model instance

Afterwards, self.instance should not be None anymore.

problem: PreemptiveRcpspProblem

retrieve_solution(result: cp.SolveResult) → PreemptiveRcpspSolution

Return a d-o solution from the variables computed by minizinc.

Parameters:

result – output of the cp.solve

Returns:

set_lexico_objective(obj: str) → None

Update internal model objective.

Parameters:

obj – a string representing the desired objective. Should be one of self.get_lexico_objectives_available().

Returns:

discrete_optimization.rcpsp.solvers.preemptive.optal.compute_binary_calendar_per_tasks(problem: PreemptiveRcpspProblem) → tuple[dict[tuple, ndarray], dict[tuple[int, int], tuple]]

Module contents