Optimization of multi-objective reliability redundancy allocation problem with non-homogeneous components using mixed redundancy strategy under uncertainty conditions

Articles in Press

Document Type : Article

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Alborz, Iran

10.24200/sci.2024.61576.7402

Abstract

In this paper, we introduce a novel multi-objective mixed integer non-linear model as an optimization multi-objective of the reliability-redundancy allocation problem (RRAP) in a series-parallel system to maximize system reliability and minimize total cost. Most studies on RRAP assume the components are homogeneous, the reliability of the components is predefined, and redundancy strategies in each subsystem are considered cold-standby or active. Each of the above assumptions serves as a constraint that doesn’t broaden solution regions. In the proposed multi-objective model, the components are heterogeneous. In addition, mixed strategies (cold-standby and active redundancies) can be used in each subsystem. The reliability of subsystem components is uncertain and is considered a decision variable. Since the proposed model is a multi-objective model, a multiple evolutionary algorithm called NSGA-II will be used to solve the proposed multi-objective mixed-integer non-linear (MOMINL) with non-homogeneous and cold standby components by problem, and the performance of the proposed mathematical model will be assessed by a well-known problem-testing method. The optimization result leads to a higher reliability value and a minimum total cost compared to the previous studies, which shows the effectiveness of the proposed model and proves that the proposed method outperforms the previous ones.

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 11 November 2024

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History

  • Receive Date: 12 January 2023
  • Revise Date: 04 October 2023
  • Accept Date: 11 November 2024

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