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Joint optimization of mission aborts and allocation of standby components considering mission loss

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  • Zhao, Xian
  • Dai, Ying
  • Qiu, Qingan
  • Wu, Yaguang

Abstract

Mission abort is an effective way to enhance system safety during mission execution. Existing multi-attempt mission abort models can be divided into two main categories according to the additivity of completed missions: non-accumulative models and completely cumulative models. This paper studies the optimal mission abort and allocation of standby components policies for the k-out-of-(n+m):F system considering partial mission loss. During each attempt, the mission abort decision is dynamically controlled via predetermined abort thresholds and the rescue procedure (RP) starts immediately upon mission abort. In most studies, after a successful RP, the system is commonly restored to an ‘as good as new’ state with the underlying assumption that the standby components are always adequate. However, due to factors such as cost and capacity, the number of standby components may be limited. This paper proposes a dynamic allocation policy of a fixed number of standby components. The aim is to determine the optimal number of the failed components be replaced after each RP. By using a recursive algorithm, mission reliability and system survivability are derived. The objective is to minimize the expected cost and balance the mission reliability and the system survivability. The advantage of the proposed policy is justified by the policy comparison. Finally, the obtained results are demonstrated considering an autonomous underwater vehicle performing a photography mission.

Suggested Citation

  • Zhao, Xian & Dai, Ying & Qiu, Qingan & Wu, Yaguang, 2022. "Joint optimization of mission aborts and allocation of standby components considering mission loss," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002551
    DOI: 10.1016/j.ress.2022.108612
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    References listed on IDEAS

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    Cited by:

    1. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2023. "Optimal aborting policy for shock exposed missions with random rescue time," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
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    3. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Using kamikaze components in multi-attempt missions with abort option," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    4. Zhao, Xian & Li, Rong & Cao, Shuai & Qiu, Qingan, 2023. "Joint modeling of loading and mission abort policies for systems operating in dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Zhao, Xian & Liu, Haoran & Wu, Yaguang & Qiu, Qingan, 2023. "Joint optimization of mission abort and system structure considering dynamic tasks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Liu, Lujie & Yang, Jun, 2023. "A dynamic mission abort policy for the swarm executing missions and its solution method by tailored deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2023. "Optimal task sequencing and aborting in multi-attempt multi-task missions with a limited number of attempts," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Yaguang Wu, 2023. "Optimal Stopping and Loading Rules Considering Multiple Attempts and Task Success Criteria," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    9. 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).
    10. Cheng, Guoqing & Li, Ling & Shangguan, Chunxia & Yang, Nan & Jiang, Bo & Tao, Ningrong, 2023. "Optimal joint inspection and mission abort policy for a partially observable system," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

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