REDUNDANCY OPTIMIZATION OF STATIC SERIES-PARALLEL RELIABILITY MODELS UNDER UNCERTAINTY

This paper extends the classical model of Ushakov on redundancy optimi zation of series-parallel static coherent reliability systems with unc ertainty in system parameters. Our objective function represents the t otal capacity of a series-parallel static system, while the decision p arameters are the nominal capacity and the availability of the element s. We obtain explicit expressions (both analytic and via efficient sim ulation) for the constraint of the program, viz, for the Cdf of the sy stem total capacity, and then show that the extended program is convex mixed-integer. Depending on whether the objective function and the as sociated constraints are analytically available or not, we suggest usi ng deterministic and stochastic (simulation) optimization approaches, respectively. The last case is associated with likelihood ratios (chan ge of probability measure). A genetic algorithm for finding the optima l redundancy is developed, and supporting numerical results are presen ted.