Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies

• Published in: Reliability engineering & system safety 2012-12, Vol.108, p.10-20
• Main Author: Safari, Jalal
• Format: Article
• Language: English
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Computer science; control theory; systems; Computer systems performance. Reliability; Decision theory. Utility theory; Exact sciences and technology; Multi-objective optimization; Non-dominate sorting genetic algorithm; Operational research and scientific management; Operational research. Management science; Redundancy allocation problem; Redundancy strategies; Reliability optimization; Reliability theory. Replacement problems; Software; Theoretical computing
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2012.06.001

This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed.