"""Run-to-failure maintenance policies."""
from __future__ import annotations
from typing_extensions import override
import numpy as np
import optype.numpy as onp
from ._base import BaseRunToFailurePolicy, OneCycleExpectedCosts
from assetlife.lifetime_models._base import ParametricLifetimeModel
from assetlife.stochastic_processes._renewal_processes import RenewalRewardProcess
from assetlife.typing import CoercibleFloat64_1D, Float64_1D, Timeline
[docs]
class OneCycleRunToFailurePolicy(BaseRunToFailurePolicy[ParametricLifetimeModel[()]]):
r"""One-cycle run-to-failure policy.
Asset is replaced upon failure with cost :math:`c_f`. Only one replacement
cycle is considered.
Parameters
----------
lifetime_model : ParametricLifetimeModel
Lifetime model representing durations between events.
"""
period_before_discounting: float
def __init__(
self,
lifetime_model: ParametricLifetimeModel[()],
period_before_discounting: float = 1.0,
) -> None:
super().__init__(lifetime_model)
self.period_before_discounting = period_before_discounting
@property
def _expected_costs(self) -> OneCycleExpectedCosts:
return OneCycleExpectedCosts(
self.baseline,
period_before_discounting=self.period_before_discounting,
)
[docs]
@override
def expected_net_present_value(
self,
tf: float,
nb_steps: int,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> tuple[Timeline, onp.Array1D[np.float64] | onp.Array2D[np.float64]]:
return self._expected_costs.expected_net_present_value(
tf, nb_steps, a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
@override
def expected_equivalent_annual_cost(
self,
tf: float,
nb_steps: int,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> tuple[Timeline, onp.Array1D[np.float64] | onp.Array2D[np.float64]]:
return self._expected_costs.expected_equivalent_annual_cost(
tf, nb_steps, a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
@override
def asymptotic_expected_net_present_value(
self,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> Float64_1D:
return self._expected_costs.asymptotic_expected_net_present_value(
a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
@override
def asymptotic_expected_equivalent_annual_cost(
self,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> Float64_1D:
return self._expected_costs.asymptotic_expected_equivalent_annual_cost(
a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
class RunToFailurePolicy(BaseRunToFailurePolicy[ParametricLifetimeModel[()]]):
r"""Run-to-failure renewal policy.
Asset is replaced upon failure with cost :math:`c_f`.
Parameters
----------
lifetime_model : ParametricLifetimeModel
Lifetime model representing durations between events.
References
----------
.. [1] Van der Weide, J. A. M., & Van Noortwijk, J. M. (2008). Renewal
theory with exponential and hyperbolic discounting. Probability in
the Engineering and Informational Sciences, 22(1), 53-74.
"""
@property
def _stochastic_reward_process(self) -> RenewalRewardProcess:
return RenewalRewardProcess(self.baseline)
[docs]
@override
def expected_net_present_value(
self,
tf: float,
nb_steps: int,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> tuple[Timeline, onp.Array1D[np.float64] | onp.Array2D[np.float64]]:
return self._stochastic_reward_process.expected_total_reward(
tf, nb_steps, a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
@override
def expected_equivalent_annual_cost(
self,
tf: float,
nb_steps: int,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> tuple[Timeline, onp.Array1D[np.float64] | onp.Array2D[np.float64]]:
return self._stochastic_reward_process.expected_equivalent_annual_worth(
tf, nb_steps, a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
@override
def asymptotic_expected_net_present_value(
self,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> Float64_1D:
return self._stochastic_reward_process.asymptotic_expected_total_reward(
a0=a0, cf=cf, discounting_rate=discounting_rate
)
[docs]
@override
def asymptotic_expected_equivalent_annual_cost(
self,
*,
cf: CoercibleFloat64_1D,
a0: CoercibleFloat64_1D | None = None,
discounting_rate: float = 0.0,
) -> Float64_1D:
return (
self._stochastic_reward_process.asymptotic_expected_equivalent_annual_worth(
a0=a0, cf=cf, discounting_rate=discounting_rate
)
)
