simopt.models.dualsourcing ========================== .. py:module:: simopt.models.dualsourcing .. autoapi-nested-parse:: Simulate periods of ordering and sales for a dual sourcing inventory problem. Module Contents --------------- .. py:class:: DualSourcingConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Dual Sourcing Inventory simulation. A model that simulates multiple periods of ordering and sales for a single-staged, dual sourcing inventory problem with stochastic demand. Returns average holding cost, average penalty cost, and average ordering cost per period. .. py:attribute:: n_days :type: Annotated[int, Field(default=1000, description='number of days to simulate', ge=1, json_schema_extra={'isDatafarmable': False})] .. py:attribute:: initial_inv :type: Annotated[int, Field(default=40, description='initial inventory', ge=0)] .. py:attribute:: cost_reg :type: Annotated[float, Field(default=100.0, description='regular ordering cost per unit', gt=0)] .. py:attribute:: cost_exp :type: Annotated[float, Field(default=110.0, description='expedited ordering cost per unit', gt=0)] .. py:attribute:: lead_reg :type: Annotated[int, Field(default=2, description='lead time for regular orders in days', ge=0)] .. py:attribute:: lead_exp :type: Annotated[int, Field(default=0, description='lead time for expedited orders in days', ge=0)] .. py:attribute:: holding_cost :type: Annotated[float, Field(default=5.0, description='holding cost per unit per period', gt=0)] .. py:attribute:: penalty_cost :type: Annotated[float, Field(default=495.0, description='penalty cost per unit per period for backlogging', gt=0)] .. py:attribute:: st_dev :type: Annotated[float, Field(default=10.0, description='standard deviation of demand distribution', gt=0)] .. py:attribute:: mu :type: Annotated[float, Field(default=30.0, description='mean of demand distribution', gt=0)] .. py:attribute:: order_level_reg :type: Annotated[int, Field(default=80, description='order-up-to level for regular orders', ge=0)] .. py:attribute:: order_level_exp :type: Annotated[int, Field(default=50, description='order-up-to level for expedited orders', ge=0)] .. py:class:: DualSourcingMinCostConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Dual Sourcing Min Cost Problem. A problem configuration that minimizes total cost for dual sourcing inventory by optimizing order levels for regular and expedited orders. .. py:attribute:: initial_solution :type: Annotated[tuple[int, int], Field(default=(50, 80), description='initial solution')] .. py:attribute:: budget :type: Annotated[int, Field(default=1000, description='max # of replications for a solver to take', gt=0, json_schema_extra={'isDatafarmable': False})] .. py:class:: DemandInputModel Bases: :py:obj:`simopt.input_models.InputModel` Input model for daily demand. .. py:attribute:: rng :type: random.Random | None :value: None .. py:method:: random(mu: float, sigma: float) -> int Generate a random variate from the input model. :returns: A random variate from the input model. :rtype: T .. py:class:: DualSourcing(fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Model` Dual Sourcing Inventory Model. A model that simulates multiple periods of ordering and sales for a single-staged, dual sourcing inventory problem with stochastic demand. Returns average holding cost, average penalty cost, and average ordering cost per period. Initialize the DualSourcing model. :param fixed_factors: Fixed factors for the model. Defaults to None. :type fixed_factors: dict, optional .. py:attribute:: class_name_abbr :type: ClassVar[str] :value: 'DUALSOURCING' Short name of the model class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Dual Sourcing' Long name of the model class. .. py:attribute:: config_class :type: ClassVar[type[pydantic.BaseModel]] Configuration class for the model. .. py:attribute:: n_rngs :type: ClassVar[int] :value: 1 Number of RNGs used to run a simulation replication. .. py:attribute:: n_responses :type: ClassVar[int] :value: 3 Number of responses (performance measures). .. py:attribute:: demand_model .. py:method:: before_replicate(rng_list: list[mrg32k3a.mrg32k3a.MRG32k3a]) -> None Set the random number generator for the demand input model. .. py:method:: replicate() -> tuple[dict, dict] Simulate a single replication for the current model factors. :param rng_list: Random number generators used to simulate the replication. :type rng_list: list[MRG32k3a] :returns: A tuple containing: - responses (dict): Performance measures of interest: - "average_holding_cost": The average holding cost over the time period. - "average_penalty_cost": The average penalty cost over the time period. - "average_ordering_cost": The average ordering cost over the time period. - gradients (dict): A dictionary of gradient estimates for each response. :rtype: tuple[dict, dict] .. py:class:: DualSourcingMinCost(name: str = '', fixed_factors: dict | None = None, model_fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Problem` Class to make dual-sourcing inventory simulation-optimization problems. Initialize a problem object. :param name: Name of the problem. :type name: str :param fixed_factors: Dictionary of user-specified problem factors. :type fixed_factors: dict | None :param model_fixed_factors: Subset of user-specified non-decision factors passed to the model. :type model_fixed_factors: dict | None .. py:attribute:: class_name_abbr :type: ClassVar[str] :value: 'DUALSOURCING-1' Short name of the problem class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Min Cost for Dual Sourcing' Long name of the problem class. .. py:attribute:: config_class :type: ClassVar[type[pydantic.BaseModel]] Configuration class for problem. .. py:attribute:: model_class :type: ClassVar[type[simopt.base.Model]] Simulation model class for problem. .. py:attribute:: n_objectives :type: ClassVar[int] :value: 1 Number of objectives. .. py:attribute:: n_stochastic_constraints :type: ClassVar[int] :value: 0 Number of stochastic constraints. .. py:attribute:: minmax :type: ClassVar[tuple[int, Ellipsis]] Indicators of maximization (+1) or minimization (-1) for each objective. .. py:attribute:: constraint_type :type: ClassVar[simopt.base.ConstraintType] Description of constraints types. .. py:attribute:: variable_type :type: ClassVar[simopt.base.VariableType] Description of variable types. .. py:attribute:: gradient_available :type: ClassVar[bool] :value: False Indicates whether the solver provides direct gradient information. .. py:attribute:: optimal_value :type: ClassVar[float | None] :value: None Optimal objective function value (if known). .. py:attribute:: optimal_solution :type: tuple | None :value: None Optimal solution if known; defaults to None. .. py:attribute:: model_default_factors :type: ClassVar[dict] Default values for overriding model-level default factors. .. py:attribute:: model_decision_factors :type: ClassVar[set[str]] Set of keys for factors that are decision variables. .. py:property:: dim :type: int Number of decision variables. .. py:property:: lower_bounds :type: tuple Lower bound for each decision variable. .. py:property:: upper_bounds :type: tuple Upper bound for each decision variable. .. py:method:: vector_to_factor_dict(vector: tuple) -> dict Convert a vector of variables to a dictionary with factor keys. :param vector: A vector of values associated with decision variables. :type vector: tuple :returns: Dictionary with factor keys and associated values. :rtype: dict .. py:method:: factor_dict_to_vector(factor_dict: dict) -> tuple Convert a dictionary with factor keys to a vector of variables. :param factor_dict: Dictionary with factor keys and associated values. :type factor_dict: dict :returns: Vector of values associated with decision variables. :rtype: tuple .. py:method:: replicate(_x: tuple) -> simopt.base.RepResult Replicate the problem for a given solution. :param x: The solution to evaluate. :type x: tuple .. py:method:: check_deterministic_constraints(x: tuple) -> bool Check if a solution `x` satisfies the problem's deterministic constraints. :param x: A vector of decision variables. :type x: tuple :returns: True if the solution satisfies all deterministic constraints; False otherwise. :rtype: bool .. py:method:: get_random_solution(rand_sol_rng: mrg32k3a.mrg32k3a.MRG32k3a) -> tuple Generate a random solution for starting or restarting solvers. :param rand_sol_rng: Random number generator used to sample the solution. :type rand_sol_rng: MRG32k3a :returns: A tuple representing a randomly generated vector of decision variables. :rtype: tuple