Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems

• Published in: Reliability engineering & system safety Vol. 111; pp. 154 - 163
• Main Authors: Cao, Dingzhou, Murat, Alper, Chinnam, Ratna Babu
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2013; Elsevier
• Subjects: Allocations; Applied sciences; Cost engineering; Decision theory. Utility theory; Decomposition; Exact sciences and technology; Heuristic methods; Mathematical models; Multi-objective optimization; Operational research and scientific management; Operational research. Management science; Optimization; Pareto optimality; Redundancy; Redundancy allocation problem; Reliability theory. Replacement problems; Series-parallel systems; Redundancy allocation problem; Multi-objective optimization; Decomposition; Series-parallel systems; Costs; Pareto optimum; Redundancy; Multiobjective programming; Series system; System reliability; Optimization; Parallel system; Optimal allocation; Heuristic method; Metamodel
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2012.09.013

This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.