Solving Redundancy Allocation Problem Using Acombing Neural Net Work and Genetic Algorithm

This thesis presents a new technique to solve the chance constraintsreliability stochastic optimization problem, combined with redundancy allocation.The objective is to determine the optimal number of components used forredundancy so as to maximize system reliability for the given chance constraints.A method is illustrated to determine optimal solutions to an n-stage series system with m chance constraints of the redundancy allocation problem. Various cases of randomness with known distributions, such as uniform, normal, and lognormal distributions, . nonlinear genetic optimization is also considered. !