simopt.models.san

Simulate duration of a stochastic activity network (SAN).

Module Contents

simopt.models.san.NUM_ARCS: Final[int] = 13

simopt.models.san.CONST_NODES: Final[list[int]] = [6, 8]

class simopt.models.san.SANConfig

Bases: pydantic.BaseModel

Configuration for the Stochastic Activity Network model.

num_nodes: Annotated[int, Field(default=9, description='number of nodes', gt=0, json_schema_extra={'isDatafarmable': False})]

arcs: Annotated[list[tuple[int, int]], Field(default=[1, 2, 1, 3, 2, 3, 2, 4, 2, 6, 3, 6, 4, 5, 4, 7, 5, 6, 5, 8, 6, 9, 7, 8, 8, 9], description='list of arcs', min_length=1)]

arc_means: Annotated[tuple[float, Ellipsis], Field(default=(1.0, ) * NUM_ARCS, description='mean task durations for each arc')]

class simopt.models.san.SANLongestPathConfig

Bases: pydantic.BaseModel

Configuration model for SAN Longest Path Problem.

Min Mean Longest Path for Stochastic Activity Network simulation-optimization problem.

initial_solution: Annotated[tuple[float, Ellipsis], Field(default_factory=lambda: (8, ) * NUM_ARCS, 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_factory=lambda: (1, ) * NUM_ARCS, description='Cost associated to each arc.')]

class simopt.models.san.SAN(fixed_factors: dict | None = None)

Bases: simopt.base.Model

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 SAN model.

Parameters:: fixed_factors – dict fixed factors of the simulation model

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

class_name: ClassVar[str] = '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”: 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.san.SANLongestPath(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] = 'SAN-1': Short name of the problem class.

class_name: ClassVar[str] = 'Min Mean Longest Path for 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.

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

class simopt.models.san.SANLongestPathStochasticConfig

Bases: pydantic.BaseModel

Configuration model for SAN Longest Path Stochastic Problem.

initial_solution: Annotated[tuple[float, Ellipsis], Field(default_factory=lambda: (8.0, ) * NUM_ARCS, 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_factory=lambda: (1.0, ) * NUM_ARCS, description='Cost associated to each arc.')]

constraint_nodes: Annotated[list[int], Field(default_factory=lambda: CONST_NODES.copy(), description='Nodes with corresponding stochastic constraints.', min_length=1)]

length_to_node_constraint: Annotated[list[float], Field(default_factory=lambda: [5.0] * len(CONST_NODES), description='Max allowable length to each constraint node.', min_length=1)]

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

Bases: simopt.base.Problem

Minimize total cost s.t. reaching certain nodes within an expected length.

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] = 'SAN-2': Short name of the problem class.

class_name: ClassVar[str] = 'Min Cost SAN with Stochastic Constraints': 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] = 2: 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.

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