simopt.model

Base class for simulation models used in simulation optimization problems.

Module Contents

class simopt.model.Model(fixed_factors: dict | None = None)

Bases: abc.ABC

Base class for simulation models used in simulation-optimization problems.

Each model defines the simulation logic behind a given problem instance.

Initialize a model object.

Parameters:

fixed_factors (dict | None, optional) – Dictionary of user-specified model factors.

class_name_abbr: ClassVar[str]

Short name of the model class.

class_name: ClassVar[str]

Long name of the model class.

config_class: ClassVar[type[pydantic.BaseModel]]

Configuration class for the model.

n_rngs: ClassVar[int]

Number of RNGs used to run a simulation replication.

n_responses: ClassVar[int]

Number of responses (performance measures).

config

name() → str

Name of model.

specifications() → dict[str, dict]

Details of each factor (for GUI, data validation, and defaults).

property factors: dict

Changeable factors of the simulation model.

model_created() → None

Hook called after the model is constructed.

Subclasses can override this to use custom input models.

abstractmethod 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.

abstractmethod replicate() → tuple[dict, dict]

Simulate a single replication for the current model factors.

Parameters:

rng_list (list[mrg32k3a.mrg32k3a.MRG32k3a]) – List of random-number generators used to generate a random replication.

Returns:

• dict: Performance measures of interest.

• dict: Gradient estimates for each response.

Return type:

tuple