simopt.models.hotel

Simulate expected revenue for a hotel.

Module Contents

class simopt.models.hotel.HotelConfig

Bases: pydantic.BaseModel

Configuration model for Hotel simulation.

A model that simulates business of a hotel with Poisson arrival rate.

num_products: Annotated[int, Field(default=56, description='number of products: (rate, length of stay)', gt=0)]

lambda_: Annotated[list[float], Field(default_factory=lambda: [x / 168 for x in _double_up([1, 2, 3, 2, 1, 0.5, 0.25, 1, 2, 3, 2, 1, 0.5, 1, 2, 3, 2, 1, 1, 2, 3, 2, 1, 2, 3, 1, 2, 1])], description='arrival rates for each product', alias='lambda')]

num_rooms: Annotated[int, Field(default=100, description='hotel capacity', gt=0)]

discount_rate: Annotated[int, Field(default=100, description='discount rate', gt=0)]

rack_rate: Annotated[int, Field(default=200, description='rack rate (full price)', gt=0)]

product_incidence: Annotated[list[list[int]], Field(default_factory=lambda: [_gen_binary_list([0, 14, 42]), _gen_binary_list([2, 24, 30]), _gen_binary_list([4, 10, 2, 20, 20]), _gen_binary_list([6, 8, 4, 8, 2, 16, 12]), _gen_binary_list([8, 6, 6, 6, 4, 6, 2, 12, 6]), _gen_binary_list([10, 4, 8, 4, 6, 4, 4, 4, 2, 8, 2]), _gen_binary_list([12, 2, 10, 2, 8, 2, 6, 2, 4, 2, 2, 4])], description='incidence matrix')]

time_limit: Annotated[list[int], Field(default_factory=lambda: [27] * 14 + [51] * 12 + [75] * 10 + [99] * 8 + [123] * 6 + [144] * 4 + [168] * 2, description='time after which orders of each product no longer arrive (e.g. Mon night stops at 3am Tues or t=27)')]

time_before: Annotated[int, Field(default=168, description='hours before t=0 to start running (e.g. 168 means start at time -168)', gt=0)]

runlength: Annotated[int, Field(default=168, description='runlength of simulation (in hours) after t=0', gt=0)]

booking_limits: Annotated[tuple[int, Ellipsis], Field(default_factory=lambda: tuple([100] * 56), description='booking limits')]

class simopt.models.hotel.HotelRevenueConfig

Bases: pydantic.BaseModel

Configuration model for Hotel Revenue Problem.

Max Revenue for Hotel Booking simulation-optimization problem.

initial_solution: Annotated[tuple[int, Ellipsis], Field(default_factory=lambda: tuple([0 for _ in range(56)]), description='initial solution')]

budget: Annotated[int, Field(default=100, description='max # of replications for a solver to take', gt=0, json_schema_extra={'isDatafarmable': False})]

class simopt.models.hotel.Hotel(fixed_factors: dict | None = None)

Bases: simopt.base.Model

A model that simulates business of a hotel with Poisson arrival rate.

Initialize the Hotel model.

Parameters:

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

class_name_abbr: ClassVar[str] = 'HOTEL'

Short name of the model class.

class_name: ClassVar[str] = 'Hotel Booking'

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).

arrival_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:

  • ”revenue”: Expected revenue.

• gradients (dict): A dictionary of gradient estimates for each

  response.

Return type:

tuple[dict, dict]

class simopt.models.hotel.HotelRevenue(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] = 'HOTEL-1'

Short name of the problem class.

class_name: ClassVar[str] = 'Max Revenue for Hotel Booking'

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] = False

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