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Optimal allocation of reliability improvement target based on the failure risk and improvement cost

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  • Kim, Kyungmee O.
  • Zuo, Ming J.

Abstract

In this paper, we consider the problem of assigning a given reliability improvement target of an existing series system to its constituent subsystems in view of the failure risk and improvement cost. Previous research has solved this problem by developing an allocation weight under the assumption that the failure risk and improvement cost are independent, and by improving every subsystem in proportion to the allocation weight. Differently, we develop an optimization model to maximize the profit from reliability improvement for the given reliability improvement target. The profit is derived from the functional relationship between the failure risk and improvement cost. The optimal solution shows that not all subsystems are improved, and the priority of subsystem improvement is determined by the difference between the failure severity and the rate of increase in the improvement cost. A numerical example is given to illustrate the advantage of the proposed method over the previous method.

Suggested Citation

  • Kim, Kyungmee O. & Zuo, Ming J., 2018. "Optimal allocation of reliability improvement target based on the failure risk and improvement cost," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 104-110.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:104-110
    DOI: 10.1016/j.ress.2018.06.024
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    References listed on IDEAS

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    1. Yadav, Om Prakash & Singh, Nanua & Goel, Parveen S., 2006. "Reliability demonstration test planning: A three dimensional consideration," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 882-893.
    2. Yadav, Om Prakash & Zhuang, Xing, 2014. "A practical reliability allocation method considering modified criticality factors," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 57-65.
    3. Kim, Kyungmee O. & Yang, Yoonjung & Zuo, Ming J., 2013. "A new reliability allocation weight for reducing the occurrence of severe failure effects," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 81-88.
    4. Kyungmee O. Kim & Ming J. Zuo, 2015. "Effects of subsystem mission time on reliability allocation," IISE Transactions, Taylor & Francis Journals, vol. 47(3), pages 285-293, March.
    5. Carmignani, Gionata, 2009. "An integrated structural framework to cost-based FMECA: The priority-cost FMECA," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 861-871.
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    Cited by:

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    2. Guo, Xiaoxue & Ding, Long & Ji, Jie & Cozzani, Valerio, 2022. "A cost-effective optimization model of safety investment allocation for risk reduction of domino effects," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Polinpapilinho F. Katina & James C. Pyne & Charles B. Keating & Dragan Komljenovic, 2021. "Complex System Governance as a Framework for Asset Management," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    4. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Feng, Qiang & Zhao, Xiujie & Fan, Dongming & Cai, Baoping & Liu, Yiqi & Ren, Yi, 2019. "Resilience design method based on meta-structure: A case study of offshore wind farm," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 232-244.
    6. Yang, Kai & Hou, Lei & Man, Jianfeng & Yu, Qiaoyan & Li, Yu & Zhang, Xinru & Liu, Jiaquan, 2023. "Supply reliability analysis of natural gas pipeline network based on demand-side economic loss risk," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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