simopt.models.cntnv

Simulate a day’s worth of sales for a newsvendor.

Module Contents

class simopt.models.cntnv.CntNVConfig

Bases: pydantic.BaseModel

Configuration model for Continuous Newsvendor simulation.

A model that simulates a day’s worth of sales for a newsvendor with a Burr Type XII demand distribution. Returns the profit, after accounting for order costs and salvage.

purchase_price: Annotated[float, Field(default=5.0, description='purchasing cost per unit', gt=0)]

sales_price: Annotated[float, Field(default=9.0, description='sales price per unit', gt=0)]

salvage_price: Annotated[float, Field(default=1.0, description='salvage cost per unit', gt=0)]

order_quantity: Annotated[float, Field(default=0.5, description='order quantity', gt=0)]

burr_c: Annotated[float, Field(default=2.0, description='Burr Type XII cdf shape parameter', gt=0, alias='Burr_c')]

burr_k: Annotated[float, Field(default=20.0, description='Burr Type XII cdf shape parameter', gt=0, alias='Burr_k')]

class simopt.models.cntnv.CntNVMaxProfitConfig

Bases: pydantic.BaseModel

Configuration model for Continuous Newsvendor Max Profit Problem.

A problem configuration that maximizes profit for a continuous newsvendor by optimizing the order quantity.

initial_solution: Annotated[tuple[float, Ellipsis], Field(default=0, description='initial solution')]

budget: Annotated[int, Field(default=1000, description='max # of replications for a solver to take', gt=0, json_schema_extra={'isDatafarmable': False})]

class simopt.models.cntnv.DemandInputModel

Bases: simopt.input_models.InputModel

Input model for Burr Type XII demand.

rng: random.Random | None = None

random(burr_c: float, burr_k: float) → float

Generate a random variate from the input model.

Returns:: A random variate from the input model.
Return type:: T

class simopt.models.cntnv.CntNV(fixed_factors: dict | None = None)

Bases: simopt.base.Model

Continuous Newsvendor Model with a Burr Type XII demand distribution.

A model that simulates a day’s worth of sales for a newsvendor with a Burr Type XII demand distribution. Returns the profit, after accounting for order costs and salvage.

Initialize the Continuous Newsvendor model.

Parameters:: fixed_factors (dict, optional) – Fixed factors for the model. Defaults to None.

class_name_abbr: ClassVar[str] = 'CNTNEWS': Short name of the model class.

class_name: ClassVar[str] = 'Continuous Newsvendor': Long name of the model class.

config_class: ClassVar[type[pydantic.BaseModel]]: Configuration class for the model.

n_rngs: ClassVar[int] = 1: Number of RNGs used to run a simulation replication.

n_responses: ClassVar[int] = 1: Number of responses (performance measures).

demand_model

before_replicate(rng_list: list[mrg32k3a.mrg32k3a.MRG32k3a]) → None

Prepare the model just before generating a replication.

Parameters:: rng_list (list[MRG32k3a]) – RNGs used to drive the simulation.
Raises:: NotImplementedError – If the subclass does not implement this hook.

replicate() → tuple[dict, dict]

Simulate a single replication for the current model factors.

Parameters:

rng_list (list[MRG32k3a]) – Random number generators used to simulate the replication.

Returns:

A tuple containing:

responses (dict): Performance measures of interest, including:
- ”profit”: Profit in this scenario.
- ”stockout_qty”: Amount by which demand exceeded supply.
- ”stockout”: Whether there was unmet demand (“Y” or “N”).
gradients (dict): Gradient estimates for each response.

Return type:

tuple[dict, dict]

class simopt.models.cntnv.CntNVMaxProfit(name: str = '', fixed_factors: dict | None = None, model_fixed_factors: dict | None = None)

Bases: simopt.base.Problem

Base class to implement simulation-optimization problems.

Initialize a problem object.

Parameters:

name (str) – Name of the problem.
fixed_factors (dict | None) – Dictionary of user-specified problem factors.
model_fixed_factors (dict | None) – Subset of user-specified non-decision factors passed to the model.

class_name_abbr: ClassVar[str] = 'CNTNEWS-1': Short name of the problem class.

class_name: ClassVar[str] = 'Max Profit for Continuous Newsvendor': Long name of the problem class.

config_class: ClassVar[type[pydantic.BaseModel]]: Configuration class for problem.

model_class: ClassVar[type[simopt.base.Model]]: Simulation model class for problem.

n_objectives: ClassVar[int] = 1: Number of objectives.

n_stochastic_constraints: ClassVar[int] = 0: Number of stochastic constraints.

minmax: ClassVar[tuple[int, Ellipsis]] = (1,): Indicators of maximization (+1) or minimization (-1) for each objective.

constraint_type: ClassVar[simopt.base.ConstraintType]: Description of constraints types.

variable_type: ClassVar[simopt.base.VariableType]: Description of variable types.

gradient_available: ClassVar[bool] = True: Indicates whether the solver provides direct gradient information.

optimal_value: ClassVar[float | None] = None: Optimal objective function value (if known).

optimal_solution: tuple | None = None: Optimal solution if known; defaults to None.

model_default_factors: ClassVar[dict]: Default values for overriding model-level default factors.

model_decision_factors: ClassVar[set[str]]: Set of keys for factors that are decision variables.

property dim: int: Number of decision variables.

property lower_bounds: tuple: Lower bound for each decision variable.

property upper_bounds: tuple: Upper bound for each decision variable.

vector_to_factor_dict(vector: tuple) → dict

Convert a vector of variables to a dictionary with factor keys.

Parameters:: vector (tuple) – A vector of values associated with decision variables.
Returns:: Dictionary with factor keys and associated values.
Return type:: dict

factor_dict_to_vector(factor_dict: dict) → tuple

Convert a dictionary with factor keys to a vector of variables.

Parameters:: factor_dict (dict) – Dictionary with factor keys and associated values.
Returns:: Vector of values associated with decision variables.
Return type:: tuple

replicate(_x: tuple) → simopt.base.RepResult

Replicate the problem for a given solution.

Parameters:: x (tuple) – The solution to evaluate.

check_deterministic_constraints(x: tuple) → bool

Check if a solution x satisfies the problem’s deterministic constraints.

Parameters:

x (tuple) – A vector of decision variables.

Returns:

True if the solution satisfies all deterministic constraints;: False otherwise.

Return type:

bool

get_random_solution(rand_sol_rng: mrg32k3a.mrg32k3a.MRG32k3a) → tuple

Generate a random solution for starting or restarting solvers.

Parameters:

rand_sol_rng (MRG32k3a) – Random number generator used to sample the solution.

Returns:

A tuple representing a randomly generated vector of decision: variables.

Return type:

tuple