Redundancy optimization of static series-parallel reliability models under uncertainty

Abstract

This paper extends the classical model of Ushakov on redundancy optimization of a series-parallel static coherent reliability systems with uncertainty in system parameters. Our objective function represents the total capacity of a series-parallel static system, while the decision parameters are the nominal capacity and the availability of the elements. We obtain explicit expressions (both analytical and via e cient simulation) for the constraint of the program, namely for the cdf of the total capacity of the system, and then show that the extended program is a convex mixed integer one. Depending on whether the objective function and the associated constraints are analytically available or not, we suggest using deterministic and stochastic (simulation) optimization approaches, respectively. The last case is associated with likelihood ratios (change of probability measure). Numerical results are presented as well.

Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm

“…Rubinstein et al [19] also used a GA to solve a redundancy problem with uncertain component properties. In their work, the expected system reliability is maximized given the uncertainty.…”

A robust loss function approach for a multi-objective redundancy allocation problem

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Genetic algorithm for chance constrained reliability stochastic optimisation problems

“…In the past three decades, numerous reliability optimisation techniques have been proposed in the field of optimal RAPs (Rubinstein et al, 1997;Tillman et al, 1977Tillman et al, , 1980. The RAP for series-parallel systems is difficult.…”