Redundancy Optimization for Multi-Performance Multi-State Series-Parallel Systems Considering Reliability Requirements

Yi Ding, Yishuang Hu and Daqing Li

Reliability Engineering and System Safety, 2021, vol. 215, issue C

Abstract: Various engineering systems operating with multiple performance measures are multi-state in nature. These systems are usually modelled as multi-performance multi-state series-parallel systems (MPS). In real-life cases, engineers need to design an optimal MPS structure by combining different versions and number of redundant multi-performance components. Moreover, to design a reliable structure, reliability evaluation is inevitable. However, the conventional redundancy optimization measured by single performance is difficult to model the optimal MPS structure. Furthermore, as the most frequently used method, randomly sizing of parent populations and stopping criterion of traditional genetic algorithm (GA) may lead to massive unnecessary structures. The computational burden may be huge for the redundancy optimization considering multi-performance measures. This paper proposes a redundancy optimization model considering multi-performance measuring systems. The optimization objective and the reliability constraints are described by multiple performance measures. The universal generating function technique is applied to evaluate the reliability. Based on the reliability, the efficient optimization method combining with ordinal optimization and genetic algorithms is proposed to design the optimal MPS structure. The ordinal optimization algorithm is employed to modify both the parent populations and stopping criterion of genetic algorithm. Finally, the proposed model and method are illustrated by numerical examples.

Keywords: Redundancy optimization; multi-performance multi-state series-parallel systems; ordinal optimization; genetic algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003926

DOI: 10.1016/j.ress.2021.107873

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