simopt.models.dynamnews ======================= .. py:module:: simopt.models.dynamnews .. autoapi-nested-parse:: Simulate sales for a newsvendor under dynamic consumer substitution. Module Contents --------------- .. py:data:: NUM_PRODUCTS :type: Final[int] :value: 10 .. py:class:: DynamNewsConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Dynamic Newsvendor simulation. A model that simulates a day's worth of sales for a newsvendor with dynamic consumer substitution. Returns the profit and the number of products that stock out. .. py:attribute:: num_prod :type: Annotated[int, Field(default=NUM_PRODUCTS, description='number of products', gt=0)] .. py:attribute:: num_customer :type: Annotated[int, Field(default=30, description='number of customers', gt=0)] .. py:attribute:: c_utility :type: Annotated[list[float], Field(default_factory=lambda: [6 + j for j in range(NUM_PRODUCTS)], description="constant of each product's utility")] .. py:attribute:: mu :type: Annotated[float, Field(default=1.0, description='mu for calculating Gumbel random variable')] .. py:attribute:: init_level :type: Annotated[list[int], Field(default_factory=lambda: [3] * NUM_PRODUCTS, description='initial inventory level')] .. py:attribute:: price :type: Annotated[list[float], Field(default_factory=lambda: [9] * NUM_PRODUCTS, description='sell price of products')] .. py:attribute:: cost :type: Annotated[list[float], Field(default_factory=lambda: [5] * NUM_PRODUCTS, description='cost of products')] .. py:class:: DynamNewsMaxProfitConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Dynamic Newsvendor Max Profit Problem. A problem configuration that maximizes profit for a dynamic newsvendor with consumer substitution by optimizing initial inventory levels. .. py:attribute:: initial_solution :type: Annotated[tuple[int, Ellipsis], Field(default_factory=lambda: (3, ) * NUM_PRODUCTS, 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:: Utility Bases: :py:obj:`simopt.input_models.InputModel` Input model for customer utility sampling. .. py:attribute:: rng :type: random.Random | None :value: None .. py:method:: random(mu: float, num_customer: int, num_prod: int, c_utility: list[float]) -> numpy.ndarray Generate a random variate from the input model. :returns: A random variate from the input model. :rtype: T .. py:class:: DynamNews(fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Model` Dynamic Newsvendor Model. A model that simulates a day's worth of sales for a newsvendor with dynamic consumer substitution. Returns the profit and the number of products that stock out. Initialize the 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: 'DYNAMNEWS' Short name of the model class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Dynamic Newsvendor' 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: 4 Number of responses (performance measures). .. py:attribute:: utility_model .. py:method:: before_replicate(rng_list: list[mrg32k3a.mrg32k3a.MRG32k3a]) -> None Prepare the model just before generating a replication. :param rng_list: RNGs used to drive the simulation. :type rng_list: list[MRG32k3a] :raises NotImplementedError: If the subclass does not implement this hook. .. 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, including: - "profit": Profit in this scenario. - "n_prod_stockout": Number of products that are out of stock. - "n_missed_orders": Number of unmet customer orders. - "fill_rate": Fraction of customer orders fulfilled. - gradients (dict): A dictionary of gradient estimates for each response. :rtype: tuple[dict, dict] .. py:class:: DynamNewsMaxProfit(name: str = '', fixed_factors: dict | None = None, model_fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Problem` Base class to implement 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: 'DYNAMNEWS-1' Short name of the problem class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Max Profit for Dynamic Newsvendor' 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]] :value: (1,) 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