discrete_optimization.lotsizing.generic_solver.cpsat package

Submodules

discrete_optimization.lotsizing.generic_solver.cpsat.backlog module

class discrete_optimization.lotsizing.generic_solver.cpsat.backlog.BacklogConstraintCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

create_backlog_cost()

create_constraint_backlog()

discrete_optimization.lotsizing.generic_solver.cpsat.changeover module

class discrete_optimization.lotsizing.generic_solver.cpsat.changeover.ChangeOverConstraintCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

changeover_vars: dict

create_changeover_constraint_and_cost(modeling: ChangeoverModel)

class discrete_optimization.lotsizing.generic_solver.cpsat.changeover.ChangeoverModel(*values)

Bases: Enum

Modeling approach for changeover costs in CP-SAT solver.

SHORTEST_PATH_BASED = 'shortest_path_based'

STATE_BASED = 'state_based'

discrete_optimization.lotsizing.generic_solver.cpsat.generic_lotsizing_cpsat module

class discrete_optimization.lotsizing.generic_solver.cpsat.generic_lotsizing_cpsat.GenericLotSizingCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: ProductionConstraintCpsat[Item], InventoryConstraintCpsat[Item], BacklogConstraintCpsat[Item], ChangeOverConstraintCpsat[Item], ParallelProductionConstraintCpsat[Item], WarmstartMixin

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.

backlog: dict[Item, list[IntVar]]

create_backlog_vars()

create_delivery_expr() → Any

create_delivery_vars()

create_inventory_vars()

create_objectives(**kwargs: Any) → None

create_production_vars()

delivery: dict[Item, list[IntVar]]

get_backlog_var(item: Item, period: int) → Any

get_delivery_var(item: Item, period: int) → Any

get_inventory_var(item: Item, period: int) → Any

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:

get_production_binary_var(item: Item, period: int) → Any

get_production_quantity_var(item: Item, period: int) → Any

get_unmet_demand(item: Item) → Any

hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='create_delivery_vars', default=True, depends_on=None, name_in_kwargs='create_delivery_vars'), EnumHyperparameter(name='modeling_changeover', default=<ChangeoverModel.SHORTEST_PATH_BASED: 'shortest_path_based'>, depends_on=None, name_in_kwargs='modeling_changeover')]

Hyperparameters available for this solver.

These hyperparameters are to be feed to **kwargs found in

• __init__()

• init_model() (when available)

• solve()

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.

init_vars_placeholder() → None

inventory: dict[Item, list[IntVar]]

objectives: dict[str, LinearExpr]

problem: GenericLotSizingProblem[Item]

production: dict[Item, list[IntVar]]

production_binary: dict[Item, list[IntVar]]

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → ProductionBasedSolution

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_warm_start(solution: ProductionBasedSolution) → None

Make the solver warm start from the given solution.

variables: dict

discrete_optimization.lotsizing.generic_solver.cpsat.generic_lotsizing_cpsat_scheduling module

Generic CP-SAT scheduling solver for lot sizing problems.

This solver uses a scheduling-based formulation where each demand is modeled as an event (interval variable) to be scheduled. This is fundamentally different from the quantity-based formulation in generic_lotsizing_cpsat.py.

Key features: - Interval variables for each demand occurrence - NoOverlap constraints for capacity - Circuit constraints for changeover sequencing - Natural representation of inventory cost as (deadline - production_time) - Efficient for problems with sparse demands or unit demands

The scheduling approach is particularly effective when: - Demands are small relative to horizon (many idle periods) - Strong changeover costs make sequencing important - Backlog is allowed (flexible timing)

class discrete_optimization.lotsizing.generic_solver.cpsat.generic_lotsizing_cpsat_scheduling.GenericLotSizingCpsatScheduling(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

Generic CP-SAT scheduling solver for lot sizing.

This solver models lot sizing as a scheduling problem where each demand is an event to be scheduled. This contrasts with the quantity-based formulation in GenericLotSizingCpsat.

Model structure: - For each demand occurrence (item, period, quantity), create demand events - Each event has a start time variable (when to produce) - Interval variables ensure no conflicts - Circuit constraint sequences productions for changeover costs - Inventory cost = (deadline - start) * holding_cost

This formulation is most efficient for: - Unit demands or small demands - Sparse demand patterns (many idle periods) - Strong changeover cost structure

demand_events: list

event_deadlines: dict

get_backlog_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_delivery_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_inventory_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_production_binary_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_production_quantity_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='unit_demand_aggregation', default=True, depends_on=None, name_in_kwargs='unit_demand_aggregation')]

Hyperparameters available for this solver.

These hyperparameters are to be feed to **kwargs found in

• __init__()

• init_model() (when available)

• solve()

init_model(**kwargs: Any) → None

Initialize the CP-SAT scheduling model.

Parameters:

**kwargs – Hyperparameters including unit_demand_aggregation

problem: GenericLotSizingProblem[Item]

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → ProductionBasedSolution

Extract solution from CP-SAT solver.

Parameters:

cpsolvercb – CP-SAT solution callback

Returns:

ProductionBasedSolution with productions and deliveries

variables: dict

discrete_optimization.lotsizing.generic_solver.cpsat.inventory module

class discrete_optimization.lotsizing.generic_solver.cpsat.inventory.InventoryConstraintCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

create_constraint_inventory()

create_inventory_cost()

discrete_optimization.lotsizing.generic_solver.cpsat.lotsizing_solver_cpsat module

class discrete_optimization.lotsizing.generic_solver.cpsat.lotsizing_solver_cpsat.LotSizingCpSatSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingGenericSolver[Item], OrtoolsCpSatSolver

discrete_optimization.lotsizing.generic_solver.cpsat.parallel_production module

class discrete_optimization.lotsizing.generic_solver.cpsat.parallel_production.ParallelProductionConstraintCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

create_constraint_parallel_production()

discrete_optimization.lotsizing.generic_solver.cpsat.production module

class discrete_optimization.lotsizing.generic_solver.cpsat.production.ProductionConstraintCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

create_constraint_production()

create_production_cost()

create_setup_cost()

Module contents

Generic CP-SAT solvers for lot sizing problems.

This module provides two CP-SAT formulations: - GenericLotSizingCpsat: Quantity-based formulation (standard MIP-like) - GenericLotSizingCpsatScheduling: Scheduling-based formulation (interval variables)

class discrete_optimization.lotsizing.generic_solver.cpsat.GenericLotSizingCpsat(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: ProductionConstraintCpsat[Item], InventoryConstraintCpsat[Item], BacklogConstraintCpsat[Item], ChangeOverConstraintCpsat[Item], ParallelProductionConstraintCpsat[Item], WarmstartMixin

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.

backlog: dict[Item, list[IntVar]]

create_backlog_vars()

create_delivery_expr() → Any

create_delivery_vars()

create_inventory_vars()

create_objectives(**kwargs: Any) → None

create_production_vars()

delivery: dict[Item, list[IntVar]]

get_backlog_var(item: Item, period: int) → Any

get_delivery_var(item: Item, period: int) → Any

get_inventory_var(item: Item, period: int) → Any

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:

get_production_binary_var(item: Item, period: int) → Any

get_production_quantity_var(item: Item, period: int) → Any

get_unmet_demand(item: Item) → Any

hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='create_delivery_vars', default=True, depends_on=None, name_in_kwargs='create_delivery_vars'), EnumHyperparameter(name='modeling_changeover', default=<ChangeoverModel.SHORTEST_PATH_BASED: 'shortest_path_based'>, depends_on=None, name_in_kwargs='modeling_changeover')]

Hyperparameters available for this solver.

These hyperparameters are to be feed to **kwargs found in

• __init__()

• init_model() (when available)

• solve()

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.

init_vars_placeholder() → None

inventory: dict[Item, list[IntVar]]

objectives: dict[str, LinearExpr]

problem: GenericLotSizingProblem[Item]

production: dict[Item, list[IntVar]]

production_binary: dict[Item, list[IntVar]]

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → ProductionBasedSolution

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_warm_start(solution: ProductionBasedSolution) → None

Make the solver warm start from the given solution.

variables: dict

class discrete_optimization.lotsizing.generic_solver.cpsat.GenericLotSizingCpsatScheduling(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)

Bases: LotSizingCpSatSolver[Item]

Generic CP-SAT scheduling solver for lot sizing.

This solver models lot sizing as a scheduling problem where each demand is an event to be scheduled. This contrasts with the quantity-based formulation in GenericLotSizingCpsat.

Model structure: - For each demand occurrence (item, period, quantity), create demand events - Each event has a start time variable (when to produce) - Interval variables ensure no conflicts - Circuit constraint sequences productions for changeover costs - Inventory cost = (deadline - start) * holding_cost

This formulation is most efficient for: - Unit demands or small demands - Sparse demand patterns (many idle periods) - Strong changeover cost structure

demand_events: list

event_deadlines: dict

get_backlog_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_delivery_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_inventory_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_production_binary_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

get_production_quantity_var(item: Item, period: int) → Any

Not applicable for scheduling formulation.

hyperparameters: list[Hyperparameter] = [CategoricalHyperparameter(name='unit_demand_aggregation', default=True, depends_on=None, name_in_kwargs='unit_demand_aggregation')]

Hyperparameters available for this solver.

These hyperparameters are to be feed to **kwargs found in

• __init__()

• init_model() (when available)

• solve()

init_model(**kwargs: Any) → None

Initialize the CP-SAT scheduling model.

Parameters:

**kwargs – Hyperparameters including unit_demand_aggregation

problem: GenericLotSizingProblem[Item]

retrieve_solution(cpsolvercb: CpSolverSolutionCallback) → ProductionBasedSolution

Extract solution from CP-SAT solver.

Parameters:

cpsolvercb – CP-SAT solution callback

Returns:

ProductionBasedSolution with productions and deliveries

variables: dict