Integrated selective maintenance and task assignment optimization for multi-state systems executing multiple missions

Weining Ma, Qin Zhang, Tangfan Xiahou, Yu Liu and Xisheng Jia

Reliability Engineering and System Safety, 2023, vol. 237, issue C

Abstract: Selective maintenance, in which an optimal subset of feasible maintenance actions is identified to be executed with the aim of maximizing the success probability of subsequent missions, has been extensively implemented in military and industrial environments. Extant selective maintenance models assumed that systems are required to execute a single mission or multiple sequential missions. In many engineering scenarios, the mission is, however, typically composed of multiple cooperative sub-tasks which can be executed parallelly. Under such a circumstance, a strategic assignment of systems to execute sub-tasks is of great significance for the mission success. To this end, a new selective maintenance model is put forth in this article to jointly optimize the maintenance action and task assignment for multi-state systems that intend to complete multiple consecutive missions. The new selective maintenance problem is formulated as a max-min optimization model to maximize the minimum probability of a system successfully completing every future mission. A cooperative co-evolutionary genetic algorithm is tailored to efficiently solve the joint optimization problem. Results from a numerical example and an air defense system demonstrate the performance of the developed algorithm, and show that the proposed method can efficiently improve the success probability of future missions by integrating selective maintenance and task assignment.

Keywords: Selective maintenance; Task assignment; Multiple consecutive missions; Multi-state system; Cooperative co-evolutionary genetic algorithm (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002442

DOI: 10.1016/j.ress.2023.109330

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