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Joint modeling of loading and mission abort policies for systems operating in dynamic environments

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  • Zhao, Xian
  • Li, Rong
  • Cao, Shuai
  • Qiu, Qingan

Abstract

Failures of safety-critical systems may cause huge economic losses and irretrievable disasters. The dynamic operating environment of such systems makes it more difficult to evaluate and control the risk of system failure. To enhance system safety, the existing literature mainly focuses on maintenance modeling and optimization, which can interrupt continuous mission execution. As an alternative, a mission can be aborted for quick response to high failure risk during mission execution prior to maintenance. In addition to mission abort, adjusting load is another effective way to control risk due to the dependence between load and failure risk. Improving load accelerates mission progress but increases system failure risk. Thus, an optimal load can be found to balance the risk of failure and the progress of the mission. This paper investigates the joint modeling of loading and mission abort policies for systems operating in dynamic environments. Information about dynamic environments, system degradation, and mission progress is integrated to guide loading and mission abort policies. The long-term average revenue rate of the system is derived and maximized by determining the optimal loads, system degradation and mission progress thresholds. Furthermore, two heuristic policies are proposed and numerical examples are given to illustrate the obtained results.

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  • 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).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005634
    DOI: 10.1016/j.ress.2022.108948
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    3. Yaguang Wu, 2023. "Optimal Stopping and Loading Rules Considering Multiple Attempts and Task Success Criteria," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    4. 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).
    5. Yan, Rui & Zhu, Xiaoping & Zhu, Xiaoning & Peng, Rui, 2023. "Joint optimisation of task abortions and routes of truck-and-drone systems under random attacks," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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