Optimizing system reliability of stochastic-flow networks

Abstract

Components assignment problem (CAP) is used for searching the best set of components that maximize the system reliability subject to one or more constraints. Many research works have been devoted to the CAP under one constraint such as assignment budget, or total lead-time. Components assignment problem (CAP) based on three constraints namely assignment cost, total lead-time, and system reliability is never discussed. Therefore, this thesis investigates a new components assignment problem called optimal components assignment problem (OCAP) that takes into account three constraints: assignment cost, total lead-time, and system reliability. Subsequently, new and efficient optimization approaches are proposed: i) the first approach is based on a random weighted genetic algorithm (RWGA) is proposed to solve the OCAP and determine the most optimal solution characterized by maximum reliability, minimum assignment cost, and minimum total lead-time ii) the second approach is based on multiobjective particle swarm optimization (MOPSO) is presented to solve the multiobjective components assignment problem (MCAP) problem. The results revealed that MOPSO is more efficient in comparison with other optimization approaches based on single or multiobjective genetic algorithms. In addition, there is no need to convert the problem into minimization or maximization and normalize the solutions based on RWGA or NSGA approaches