4.7 Article

Joint optimization of mission abort and system structure considering dynamic tasks

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ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2023.109128

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Mission abort; Dynamic tasks; Mission reliability; System survivability; Warm standby

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This paper focuses on the optimization of condition-based mission abort policies and system structure in a work system with random task arrivals. Different decision criteria are considered and corresponding abort policies are proposed. Mission reliability and system survivability are derived using recursive methods, and the objective is to minimize the expected total cost while balancing mission reliability and system survivability by jointly optimizing the abort policies and system structure.
Mission abort has recently attracted considerable attention to enhance the safety of critical systems during the primary mission (PM). Most of the existing research focuses on mission abort policies for systems performing a deterministic PM, i.e., operating for a fixed mission duration or completing a specified amount of work. However, in practice, systems are commonly required to perform dynamic tasks. This paper first makes advancements by jointly optimizing condition-based mission abort policies and system structure for the l-out-of-n: G warm standby system, where the dynamic arrival of tasks with a random amount of work is considered. In such systems, some components are initially in active mode, and the remaining warm standby components provide fault tolerance. Two types of mission success criteria are considered and corresponding mission abort policies are proposed based on different decision criteria. Mission reliability (MR) and system survivability (SS) are derived using recursive methods, considering the random switching of the active, idle, and warm standby modes under dynamic arrival of tasks. Mission abort policies and system structure are jointly optimized to balance MR and SS with the objective of minimizing the expected total cost. An example of a multiprocessor system is presented to illustrate the proposed model.

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