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Balancing mission success probability and risk of system loss by allocating redundancy in systems operating with a rescue option

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  • Levitin, Gregory
  • Finkelstein, Maxim
  • Li, Yan-Feng

Abstract

In many real-world safety-critical applications, a rescue procedure aimed at saving a system can be activated after its failure to execute a primary mission. For such systems, it is essential to keep the balance between the mission success probability and the system survival probability, as both of these contradicting metrics are extremely important in practice. This balance is achieved in the paper by solving the corresponding constrained redundancy optimization problem utilizing the innovative algorithmic approach. The developed methodology is applied to multicomponent systems with overlapping sets of components involved in primary and rescue phases of a mission, respectively. Each component is a subsystem that consists of parallel heterogeneous elements. The random environment in which the system operates is modeled by the Poisson shock process, whereas each shock can result in failures with given probabilities. The detailed numerical example illustrating optimal redundancy solutions is presented. Further directions for generalizations and applications of the obtained results are discussed.

Suggested Citation

  • Levitin, Gregory & Finkelstein, Maxim & Li, Yan-Feng, 2020. "Balancing mission success probability and risk of system loss by allocating redundancy in systems operating with a rescue option," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019308361
    DOI: 10.1016/j.ress.2019.106694
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    References listed on IDEAS

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    1. Levitin, Gregory & Xing, Liudong & Xiang, Yanping & Dai, Yuanshun, 2021. "Mixed failure-driven and shock-driven mission aborts in heterogeneous systems with arbitrary structure," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    2. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Mission aborting and system rescue for multi-state systems with arbitrary structure," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Liu, Lujie & Yang, Jun & Yan, Bingxin, 2024. "A dynamic mission abort policy for transportation systems with stochastic dependence by deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Wang, Xiaolin & Xu, Jihui & Zhang, Lei & Wang, Ning, 2023. "Mission success probability optimizing of phased mission system balancing the phase backup and system risk: A novel GERT mechanism," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    5. Dui, Hongyan & Lu, Yaohui & Chen, Liwei, 2024. "Importance-based system cost management and failure risk analysis for different phases in life cycle," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Dui, Hongyan & Zhang, Yulu & Bai, Guanghan, 2024. "Analysis of variable system cost and maintenance strategy in life cycle considering different failure modes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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