simopt.models.tableallocation ============================= .. py:module:: simopt.models.tableallocation .. autoapi-nested-parse:: Simulate multiple periods of arrival and seating at a restaurant. Module Contents --------------- .. py:class:: TableAllocationConfig Bases: :py:obj:`pydantic.BaseModel` Configuration for the Table Allocation model. .. py:attribute:: n_hours :type: Annotated[float, Field(default=5.0, description='number of hours to simulate', gt=0)] .. py:attribute:: capacity :type: Annotated[int, Field(default=80, description='maximum capacity of restaurant', gt=0)] .. py:attribute:: table_cap :type: Annotated[list[int], Field(default=[2, 4, 6, 8], description='seating capacity of each type of table')] .. py:attribute:: lambda_ :type: Annotated[list[float], Field(default=[3, 6, 3, 3, 2, 4 / 3, 6 / 5, 1], description='average number of arrivals per hour', alias='lambda')] .. py:attribute:: service_time_means :type: Annotated[list[float], Field(default=[20, 25, 30, 35, 40, 45, 50, 60], description='mean service time (in minutes)')] .. py:attribute:: table_revenue :type: Annotated[list[float], Field(default=[15, 30, 45, 60, 75, 90, 105, 120], description='revenue earned for each group size')] .. py:attribute:: num_tables :type: Annotated[list[int], Field(default=[10, 5, 4, 2], description='number of tables of each capacity')] .. py:class:: TableAllocationMaxRevConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Table Allocation Max Revenue Problem. Max Revenue for Restaurant Table Allocation simulation-optimization problem. .. py:attribute:: initial_solution :type: Annotated[tuple[int, Ellipsis], Field(default=(10, 5, 4, 2), 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:: TableAllocation(fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Model` Table Allocation Model. A model that simulates a table capacity allocation problem at a restaurant with a homogenous Poisson arrvial process and exponential service times. Returns expected maximum revenue. Initialize the Table Allocation 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: 'TABLEALLOCATION' Short name of the model class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Restaurant Table Allocation' 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: 3 Number of RNGs used to run a simulation replication. .. py:attribute:: n_responses :type: ClassVar[int] :value: 2 Number of responses (performance measures). .. py:attribute:: arrival_time_model .. py:attribute:: arrival_number_model .. py:attribute:: group_size_model .. py:attribute:: service_time_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: - "total_revenue": Total revenue earned over the simulation period. - "service_rate": Fraction of customer arrivals that are seated. - gradients (dict): A dictionary of gradient estimates for each response. :rtype: tuple[dict, dict] .. py:class:: TableAllocationMaxRev(name: str = '', fixed_factors: dict | None = None, model_fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Problem` Class to make table allocation 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: 'TABLEALLOCATION-1' Short name of the problem class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Max Revenue for Restaurant Table Allocation' 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