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A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems

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  • Zaretalab, Arash
  • Sharifi, Mani
  • Guilani, Pedram Pourkarim
  • Taghipour, Sharareh
  • Niaki, Seyed Taghi Akhavan

Abstract

This paper presents a multi-objective availability-redundancy allocation optimization model for a hyper-system. The hyper-system consists of B systems with shared resources. The structure of the systems is series-parallel subsystems consisting of multi-failure and multi-state components. The components may be purchased from different suppliers based on their price and discounts. It is assumed that technical and organizational activities continuously affect the components' failure rates and the subsystems' working conditions before starting the system's mission horizon. The model aims to find the optimal number and the type of the subsystems' components for all systems from each supplier and the level of the technical and organizational activities. The problem is classified as an NP-hard class of problems; thus, four multi-objective meta-heuristics are adapted to solve the proposed model. These four meta-heuristics are non-dominated sorting genetic algorithm type II (NSGA-II) and type III (NSGA-III), non-dominated ranking genetic algorithm (NRGA), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The parameters of all algorithms are calibrated using response surface methodology. Moreover, a method based on a sequential combination of multi-objective evolutionary algorithms and data clustering is used on the Pareto solutions to yield a smaller and more manageable set of prospective solutions. The results showed the superiority of the NSGA-III and the MOEA/D algorithms to solve the presented model.

Suggested Citation

  • Zaretalab, Arash & Sharifi, Mani & Guilani, Pedram Pourkarim & Taghipour, Sharareh & Niaki, Seyed Taghi Akhavan, 2022. "A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000692
    DOI: 10.1016/j.ress.2022.108394
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    Cited by:

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    3. Mellal, Mohamed Arezki & Zio, Enrico & Al-Dahidi, Sameer & Masuyama, Naoki & Nojima, Yusuke, 2023. "System design optimization with mixed subsystems failure dependencies," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Eslami Baladeh, Aliakbar & Taghipour, Sharareh, 2022. "Reliability optimization of dynamic k-out-of-n systems with competing failure modes," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    5. Xia, Tangbin & Cao, Lei & Xu, Yuhui & Zhang, Kaigan & Chen, Zhen & Pan, Ershun & Xi, Lifeng, 2024. "Multi-level maintenance and inventory joint optimization for a k-out-of-n hyper-system considering the selection of suppliers with incentive discount policies," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Marwa F. Mohamed & Mohamed Meselhy Eltoukhy & Khalil Al Ruqeishi & Ahmad Salah, 2023. "An Adapted Multi-Objective Genetic Algorithm for Healthcare Supplier Selection Decision," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    7. Wang, Chaonan & Wang, Shuli & Xing, Liudong & Guan, Quanlong, 2023. "Efficient performability analysis of dynamic multi-state k-out-of-n: G systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Yeh, Wei-Chang, 2022. "BAT-based algorithm for finding all Pareto solutions of the series-parallel redundancy allocation problem with mixed components," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    9. Lin, Zhixian & Tao, Longlong & Wang, Shaoxuan & Yong, Nuo & Xia, Dongqin & Wang, Jianye & Ge, Daochuan, 2024. "A subset simulation analysis framework for rapid reliability evaluation of series-parallel cold standby systems," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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