discrete_optimization.rcalbp_l.solvers package

Submodules

discrete_optimization.rcalbp_l.solvers.cpsat module

class discrete_optimization.rcalbp_l.solvers.cpsat.CpSatRCALBPLSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: AllocationCpSatSolver[Tuple[int, int], int], SchedulingCpSatSolver[Tuple[int, int]], WarmstartMixin

add_cycle_time_lower_bound(p: int, lower_bound: int)

Ensures cycle time monotonicity across independent chunk boundaries.

constraint_only_one_station_allocation()

create_cumulative_resource_constraint()

create_cycle_time_variables()

create_heuristic_target_reached_constraints(apply_heuristic: bool = True)

create_main_unfolded_intervals()

create_precedence_constraints()

create_resource_dispatch()

create_zone_blocking()

Be careful on the fact that this constraint only is valid with capa==1 for now

fix_allocations(wks: dict)

Hard-fixes the allocation variables to a known configuration. This transforms the complex ALBP problem into a scheduling problem.

fix_allocations_and_resources(wks: dict, raw: dict)

Hard-fixes the allocation and resource variables to a known configuration. This transforms the complex ALBP problem into a purely independent scheduling problem.

get_task_start_or_end_variable(task: Task, start_or_end: StartOrEnd) → LinearExprT

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

Parameters:

• task

• start_or_end

Returns:

get_task_unary_resource_is_present_variable(task: Task, unary_resource: WorkStation) → 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(minimize_used_cycle_time: bool = False, add_heuristic_constraint: bool = True, **kwargs: Any) → None

Init cp model and reset stored variables if any.

objective_value(minimize_used_cycle_time: bool = False)

problem: RCALBPLProblem

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → RCALBPLSolution

Parses the CP-SAT result back into a native RCALBPLSolution.

set_warm_start(solution: RCALBPLSolution) → None

Injects an almost complete model state as a hint to the CP-SAT solver.

property subset_tasks_of_interest: Iterable[Tuple[int, int]]

Subset of tasks of interest for the allocation…

variables: dict

discrete_optimization.rcalbp_l.solvers.meta_solvers module

class discrete_optimization.rcalbp_l.solvers.meta_solvers.BackwardSequentialRCALBPLSolver(problem: RCALBPLProblem, future_chunk_size: int = 5, phase2_chunk_size: int = 10, time_limit_phase1: int = 120, time_limit_phase2: int = 30, use_sgs_warm_start: bool = True, **kwargs: Any)

Bases: SolverDO

Independent Chunk Backward Reasoning Solver with SGS Warm-Starting.

problem: RCALBPLProblem

solve(**kwargs: Any) → ResultStorage

Generic solving function.

Parameters:

• callbacks – list of callbacks used to hook into the various stage of the solve

• **kwargs – any argument specific to the solver

Solvers deriving from SolverDo should use callbacks methods .on_step_end(), … during solve(). But some solvers are not yet updated and are just ignoring it.

Returns (ResultStorage): a result object containing potentially a pool of solutions to a discrete-optimization problem

class discrete_optimization.rcalbp_l.solvers.meta_solvers.BackwardSequentialRCALBPLSolverSGS(problem: RCALBPLProblem, future_chunk_size: int = 5, phase2_chunk_size: int = 10, time_limit_phase1: int = 120, time_limit_phase2: int = 30, use_sgs_warm_start: bool = True, **kwargs: Any)

Bases: BackwardSequentialRCALBPLSolver

Independent Chunk Backward Reasoning Solver with SGS Warm-Starting.

problem: RCALBPLProblem

class discrete_optimization.rcalbp_l.solvers.meta_solvers.BalancedBackwardSequentialRCALBPLSolver(problem: RCALBPLProblem, future_chunk_size: int = 5, phase2_chunk_size: int = 10, time_limit_phase1: int = 120, time_limit_phase2: int = 30, use_sgs_warm_start: bool = True, **kwargs: Any)

Bases: BackwardSequentialRCALBPLSolver

Upgraded Backward Solver using the ‘Two-Period Extremes’ heuristic. Phase 1 isolates ONLY the first unstable period and the final steady-state period.

solve(**kwargs: Any) → ResultStorage

Generic solving function.

Parameters:

• callbacks – list of callbacks used to hook into the various stage of the solve

• **kwargs – any argument specific to the solver

Solvers deriving from SolverDo should use callbacks methods .on_step_end(), … during solve(). But some solvers are not yet updated and are just ignoring it.

Returns (ResultStorage): a result object containing potentially a pool of solutions to a discrete-optimization problem

discrete_optimization.rcalbp_l.solvers.optal module

class discrete_optimization.rcalbp_l.solvers.optal.OptalRCALBPLSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: AllocationOptalSolver[Tuple[int, int], int], SchedulingOptalSolver[Tuple[int, int]]

constraint_only_one_station_allocation()

create_cumulative_resource_constraint()

create_cycle_time_variables()

create_heuristic_target_reached_constraints(apply_heuristic: bool = True)

create_main_unfolded_intervals()

create_precedence_constraints()

create_resource_dispatch()

create_zone_blocking()

get_task_interval_variable(task: Task) → cp.IntervalVar

Retrieve the interval variable of given task.

get_task_unary_resource_is_present_variable(task: Task, unary_resource: WorkStation) → 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.

objective_value()

problem: RCALBPLProblem

retrieve_solution(result: cp.SolveResult) → RCALBPLSolution

Parses the OptalCP result back into a native RCALBPLSolution, translating absolute unfolded times back to relative workstation times.

variables: dict

class discrete_optimization.rcalbp_l.solvers.optal.OptalRCALBPLSolverV2(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: AllocationOptalSolver[Tuple[int, int], int], SchedulingOptalSolver[Tuple[int, int]]

create_cumulative_resource_constraint()

create_cycle_time_variables()

create_heuristic_target_reached_constraints(apply_heuristic: bool = True)

create_main_unfolded_intervals()

create_precedence_constraints()

create_resource_dispatch()

create_zone_blocking()

get_task_interval_variable(task: Task) → cp.IntervalVar

Retrieve the interval variable of given task.

get_task_unary_resource_is_present_variable(task: Task, unary_resource: WorkStation) → 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.

objective_value()

problem: RCALBPLProblem

retrieve_solution(result: cp.SolveResult) → RCALBPLSolution

Parses the OptalCP result back into a native RCALBPLSolution, translating absolute unfolded times back to relative workstation times.

variables: dict

discrete_optimization.rcalbp_l.solvers.pareto_postprocess module

class discrete_optimization.rcalbp_l.solvers.pareto_postprocess.DpRCALBPLPostProSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: DpSolver

init_model(from_solution: RCALBPLSolution, max_nb_adjustments: int = None, **kwargs: Any) → None

Initialize internal model used to solve.

Can initialize a ortools, milp, gurobi, … model.

problem: RCALBPLProblem

retrieve_solution(sol: Solution) → RCALBPLSolution

solution: RCALBPLSolution

class discrete_optimization.rcalbp_l.solvers.pareto_postprocess.RampUpParetoSolverPostpro(problem: RCALBPLProblem, 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.

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:

static 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(from_solution: RCALBPLSolution, **kwargs: Any) → None

Init cp model and reset stored variables if any.

problem: RCALBPLProblem

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → RCALBPLSolution

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:

solution: RCALBPLSolution

Module contents