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Optimal aborting strategy for three-phase missions performed by multiple units

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  • Levitin, Gregory
  • Finkelstein, Maxim
  • Xiang, Yanping

Abstract

It is often reasonable to abort a mission before its completion if the consequences of a failure in the rest of a mission time outweigh the potential benefits. This paper considers the multi-attempt missions that are performed by a multi-unit system when each unit independently can complete a mission. To model the real-life situations when the successful completion of the operation phase does not guarantee the mission success, a post-operation final phase is introduced. A mission succeeds if, at least, one of the units succeeds to complete this final phase. A unit can start the new attempt if it has been saved during the rescue phase activated upon the operation abort or after completing the previous attempt that did not achieve a mission goal. A system operates in a random environment modelled by a shock process. Units can fail under shocks with probabilities increasing with the number of survived shocks. The maximum number of shocks allowed before the mission abort is used as the optimization parameter for the optimal aborting strategy that achieves the balance between the mission success probability and the expected number of units lost while executing the mission. The developed approach is illustrated by the detailed examples showing, specifically, that the optimal number of attempts under the risk-avert policy can be obtained along with the optimal abort policy.

Suggested Citation

  • Levitin, Gregory & Finkelstein, Maxim & Xiang, Yanping, 2021. "Optimal aborting strategy for three-phase missions performed by multiple units," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
  • Handle: RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020308942
    DOI: 10.1016/j.ress.2020.107408
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    References listed on IDEAS

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    Cited by:

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    2. Matsuoka, Takeshi, 2023. "Reliability analysis of a BWR plant system at startup stage  - analysis by the GO-FLOW methodology with consideration of loop structures and phased mission problem -," Reliability Engineering and System Safety, Elsevier, vol. 233(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. Zhao, Xian & Chai, Xiaofei & Sun, Jinglei & Qiu, Qingan, 2021. "Joint optimization of mission abort and component switching policies for multistate warm standby systems," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    5. Zhao, Xian & Lv, Zuheng & Qiu, Qingan & Wu, Yaguang, 2023. "Designing two-level rescue depot location and dynamic rescue policies for unmanned vehicles," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. 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).

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