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- W4308388214 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. • Joint loading and mission abort policies are designed for safety-critical systems. • Dynamic loading and mission abort decisions are optimized considering dynamic environments. • The long-term average revenue rate of the system is derived adopting the semi-regenerative process technique. • The performance of the optimal policy is compared with two heuristic policies." @default.
- W4308388214 created "2022-11-11" @default.
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- W4308388214 date "2023-02-01" @default.
- W4308388214 modified "2023-10-15" @default.
- W4308388214 title "Joint modeling of loading and mission abort policies for systems operating in dynamic environments" @default.
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- W4308388214 doi "https://doi.org/10.1016/j.ress.2022.108948" @default.
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