On the optimal redundancy allocation for multi-state series–parallel systems under epistemic uncertainty. (December 2019)

• Record Type: Journal Article
• Authors: Sun, Mu-Xia; Li, Yan-Fu; Zio, Enrico
• Is Part Of: Reliability engineering & system safety. Volume 192(2019)
• Publication Date: 2019-12
• Subjects: Redundancy allocation -- Multi-state series–parallel systems -- Epistemic uncertainty -- Stochastic dominance -- Approximate system availability analysis -- Repair algorithm -- Local search; CDF cumulative probability distribution function -- GA genetic algorithm -- MOEA multi-objective evolutionary algorithm -- RAP redundancy allocation problem -- MSS multi-state system -- MSSPS multi-state series parallel system -- MSSPS RAP EU MSSPS RAP under epistemic uncertainty -- RV random variable -- LP linear programming -- iff if and only if; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2017.11.025
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: We study the redundancy allocation problem for multi-state series–parallel systems under epistemic uncertainty. The objective is to simultaneously maximize the supremum and infimum of system availability. We propose a modified NSGA-II with targeted designs of repair and local search operation. The algorithm is compared with standard NSGA-II. The results show that the proposed algorithm outperforms the standard NSGA-II. Abstract: In this paper, we study the redundancy allocation problem (RAP) for multi-state series–parallel systems (MSSPSs). For each multi-state component, the exact values of its state probabilities are assumed to be unknown, due to epistemic uncertainty (EU), and only conservative lower and upper bounds of them are given. The objective of the RAP is to simultaneously maximize the supremum and infimum of the system's uncertain availability, under a cost constraint. The problem is two-stage and multi-objective. In this work, we: 1. provide a linear-time algorithm to obtain the component state distribution, under which the uncertain system availability will be at its supremum or infimum; 2. show that the problem is reducible to one-stage; 3. analyze the landscape of MSSPS RAP under EU and propose a modified NSGA-II, with targeted designs of repair and local search operation. The proposed algorithm is compared with standard NSGA-II on multiple benchmarks. The results show that the proposed algorithm significantly outperforms the standard NSGA-II in both optimality and time efficiency.