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System redundancy optimization with uncertain stress-based component reliability: Minimization of regret

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  • Chatwattanasiri, Nida
  • Coit, David W.
  • Wattanapongsakorn, Naruemon

Abstract

System reliability design optimization models have been developed for systems exposed to changing and diverse stress and usage conditions. Uncertainty is addressed through defining a future operating environment where component stresses have shifted or changed for different future usage scenarios. Due to unplanned variations or changing environments and operating stresses, component and system reliability often cannot be predicted or estimated without uncertainty. Component reliability can vary due to a relative increase/decrease of stresses or operating conditions. The uncertain parameters of stresses have been incorporated directly into the new decision-making model. Risk analysis perspectives, including risk-neutral and risk-averse, are considered as system reliability objective functions. A regret function is defined, and minimization of the maximum regret provides an objective function based on random future usage stresses. This is an entirely new formulation of the redundancy allocation problem, but it is a relevant one for some problem domains. The redundancy allocation problem is solved to select the best design solution when there are multiple choices of components and system-level constraints. Nonlinear programming and a neighborhood search heuristic method are recommended to obtain the integer solutions for risk-based formulations.

Suggested Citation

  • Chatwattanasiri, Nida & Coit, David W. & Wattanapongsakorn, Naruemon, 2016. "System redundancy optimization with uncertain stress-based component reliability: Minimization of regret," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 73-83.
    • Handle: RePEc:eee:reensy:v:154:y:2016:i:c:p:73-83
    DOI: 10.1016/j.ress.2016.05.011
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    Cited by:

    1. Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2019. "A heuristic survival signature based approach for reliability-redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 511-517.
    2. Behzad Karimi & Seyed Taghi Akhavan Niaki & Seyyed Masih Miriha & Mahsa Ghare Hasanluo & Shima Javanmard, 2019. "A weighted K-means clustering approach to solve the redundancy allocation problem of systems having components with different failures," Journal of Risk and Reliability, , vol. 233(6), pages 925-942, December.
    3. Zhang, Hanxiao & Li, Yan-Fu, 2022. "Robust optimization on redundancy allocation problems in multi-state and continuous-state series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Ling, Chunyan & Yang, Lechang & Feng, Kaixuan & Kuo, Way, 2023. "Survival signature based robust redundancy allocation under imprecise probability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Wu, Xin-yang & Wu, Xiao-yue & Balakrishnan, Narayanaswamy, 2018. "Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 266-276.
    7. Fu, Yuqiang & Wang, Jun, 2022. "Optimum periodic maintenance policy of repairable multi-component system with component reallocation and system overhaul," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Soheil Azizi & Milad Mohammadi, 2023. "Strategy selection for multi-objective redundancy allocation problem in a k-out-of-n system considering the mean time to failure," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 1021-1044, June.
    9. Fiondella, Lance & Lin, Yi-Kuei & Pham, Hoang & Chang, Ping-Chen & Li, Chendong, 2017. "A confidence-based approach to reliability design considering correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 102-114.
    10. Bei, Xiaoqiang & Zhu, Xiaoyan & Coit, David W., 2019. "A risk-averse stochastic program for integrated system design and preventive maintenance planning," European Journal of Operational Research, Elsevier, vol. 276(2), pages 536-548.
    11. Ma, Weining & Zhang, Qin & Xiahou, Tangfan & Liu, Yu & Jia, Xisheng, 2023. "Integrated selective maintenance and task assignment optimization for multi-state systems executing multiple missions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Guilani, Pardis Pourkarim & Juybari, Mohammad N. & Ardakan, Mostafa Abouei & Kim, Heungseob, 2020. "Sequence optimization in reliability problems with a mixed strategy and heterogeneous backup scheme," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    13. 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).

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