Genetic algorithm for chance constrained reliability stochastic optimisation problems

Abstract

This paper addresses the chance constrained reliability stochastic optimisation problem, in which the objective is to maximise system reliability for the given chance constraints. A problem specific stochastic simulationbased genetic algorithm (GA) is developed for finding optimal redundancy to an n-stage series system with m-chance constraints of the redundancy allocation problem. As GA is a proven robust evolutionary optimisation search technique for solving various reliability optimisation problems and the Monte Carlo (MC) simulation, which is a flexible tool for checking feasibility of chance constraints, we have effectively combined GA and MC simulation in the proposed algorithm. The effectiveness of the proposed algorithm is illustrated for a four-stage series system with two chance constraints. . Currently, he is affiliated with CENTRUM Católica, Graduate School of Business, Pontificia Universidad Católica del Perú as a Professor and Principal Researcher. His research interests are stochastic and fractional programming theory and applications, stochastic data envelopment analysis (DEA), service quality gap analysis, six sigma and financial optimisation. He has published over 40 research papers in journals and proceedings of repute. Some of his contributions can be seen in