simopt.models.fixedsan

Simulate duration of a stochastic activity network (SAN).

Module Contents

simopt.models.fixedsan.NUM_ARCS: Final[int] = 13

class simopt.models.fixedsan.FixedSANConfig

Bases: pydantic.BaseModel

Configuration model for Fixed Stochastic Activity Network simulation.

A model that simulates a stochastic activity network problem with tasks that have exponentially distributed durations, and the selected means come with a cost.

num_arcs: Annotated[int, Field(default=NUM_ARCS, description='number of arcs', gt=0)]

num_nodes: Annotated[int, Field(default=9, description='number of nodes', gt=0)]

arc_means: Annotated[tuple[float, Ellipsis], Field(default=(1, ) * NUM_ARCS, description='mean task durations for each arc')]

class simopt.models.fixedsan.FixedSANLongestPathConfig

Bases: pydantic.BaseModel

Configuration model for Fixed SAN Longest Path Problem.

Min Mean Longest Path for Fixed Stochastic Activity Network problem.

initial_solution: Annotated[tuple[float, Ellipsis], Field(default=(10, ) * 13, description='initial solution')]

budget: Annotated[int, Field(default=10000, description='max # of replications for a solver to take', gt=0, json_schema_extra={'isDatafarmable': False})]

arc_costs: Annotated[tuple[float, Ellipsis], Field(default=(1, ) * 13, description='cost associated to each arc')]

class simopt.models.fixedsan.FixedSAN(fixed_factors: dict | None = None)

Bases: simopt.base.Model

Fixed Stochastic Activity Network (SAN) Model.

A model that simulates a stochastic activity network problem with tasks that have exponentially distributed durations, and the selected means come with a cost.

Initialize the Fixed Stochastic Activity Network model.

Parameters:

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

class_name_abbr: ClassVar[str] = 'FIXEDSAN'

Short name of the model class.

class_name: ClassVar[str] = 'Fixed Stochastic Activity Network'

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).

time_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:

  • ”longest_path_length”: The length or duration of the longest path.

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

  each response.

Return type:

tuple[dict, dict]

class simopt.models.fixedsan.FixedSANLongestPath(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] = 'FIXEDSAN-1'

Short name of the problem class.

class_name: ClassVar[str] = 'Min Mean Longest Path for Fixed Stochastic Activity Network'

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] = 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.

check_arc_costs() → bool

Check if all arc costs are positive and match the number of arcs.

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