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Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm

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  • Tavakkoli-Moghaddam, R.
  • Safari, J.
  • Sassani, F.

Abstract

This paper proposes a genetic algorithm (GA) for a redundancy allocation problem for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. Majority of the solution methods for the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. Thus, the problem is to select the best redundancy strategy, component, and redundancy level for each subsystem in order to maximize the system reliability under system-level constraints. This belongs to the NP-hard class of problems. Due to its complexity, it is so difficult to optimally solve such a problem by using traditional optimization tools. It is demonstrated in this paper that GA is an efficient method for solving this type of problems. Finally, computational results for a typical scenario are presented and the robustness of the proposed algorithm is discussed.

Suggested Citation

  • Tavakkoli-Moghaddam, R. & Safari, J. & Sassani, F., 2008. "Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 550-556.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:4:p:550-556
    DOI: 10.1016/j.ress.2007.02.009
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    References listed on IDEAS

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    1. Yalaoui, Alice & Chu, Chengbin & Châtelet, Eric, 2005. "Reliability allocation problem in a series–parallel system," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 55-61.
    2. Gen, Mitsuo & Yun, YoungSu, 2006. "Soft computing approach for reliability optimization: State-of-the-art survey," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1008-1026.
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