simopt.models.network

Simulate messages being processed in a queueing network.

Module Contents

simopt.models.network.NUM_NETWORKS: Final = 10

class simopt.models.network.NetworkConfig

Bases: pydantic.BaseModel

Configuration for the queueing network model.

process_prob: Annotated[list[float], Field(default_factory=lambda: [0.1] * NUM_NETWORKS, description='probability that a message will go through a particular network i')]

cost_process: Annotated[list[float], Field(default_factory=lambda: [0.1 / (x + 1) for x in range(NUM_NETWORKS)], description='message processing cost of network i')]

cost_time: Annotated[list[float], Field(default_factory=lambda: [0.005] * NUM_NETWORKS, description='cost for the length of time a message spends in a network i per each unit of time')]

mode_transit_time: Annotated[list[float], Field(default_factory=lambda: [x + 1 for x in range(NUM_NETWORKS)], description='mode time of transit for network i following a triangular distribution')]

lower_limits_transit_time: Annotated[list[float], Field(default_factory=lambda: [0.5 + x for x in range(NUM_NETWORKS)], description='lower limits for the triangular distribution for the transit time')]

upper_limits_transit_time: Annotated[list[float], Field(default_factory=lambda: [1.5 + x for x in range(NUM_NETWORKS)], description='upper limits for the triangular distribution for the transit time')]

arrival_rate: Annotated[float, Field(default=1.0, description='arrival rate of messages following a Poisson process', gt=0)]

n_messages: Annotated[int, Field(default=1000, description='number of messages that arrives and needs to be routed', gt=0)]

n_networks: Annotated[int, Field(default=NUM_NETWORKS, description='number of networks', gt=0)]

class simopt.models.network.NetworkMinTotalCostConfig

Bases: pydantic.BaseModel

Configuration model for Network Min Total Cost Problem.

Min Total Cost for Communication Networks System simulation-optimization problem.

initial_solution: Annotated[tuple[float, Ellipsis], Field(default_factory=lambda: (0.1, ) * NUM_NETWORKS, 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.network.RouteInputModel

Bases: simopt.input_models.InputModel

Input model for routing choices in the network.

rng: random.Random | None = None

random(choices: list[int], weights: list[float], k: int) → list[int]

Generate a random variate from the input model.

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

class simopt.models.network.Network(fixed_factors: dict | None = None)

Bases: simopt.base.Model

Simulate messages being processed in a queueing network.

Initialize the Network model.

Parameters:: fixed_factors (dict) – Fixed factors for the model.

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

class_name: ClassVar[str] = 'Communication Networks System': Long name of the model class.

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

n_rngs: ClassVar[int] = 3: Number of RNGs used to run a simulation replication.

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

arrival_model

route_model

service_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 measure of interest, including:
- ”total_cost”: Total cost spent to route all messages.
gradients (dict): A dictionary of gradient estimates for
each response.

Return type:

tuple[dict, dict]

class simopt.models.network.NetworkMinTotalCost(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] = 'NETWORK-1': Short name of the problem class.

class_name: ClassVar[str] = 'Min Total Cost for Communication Networks System': 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]]: 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