Joint Optimization of Selective Maintenance Decision and Mission Assignment for Equipment Groups

被引：0
|
作者：
Ma W. [1 ]
Hu Q. [1 ]
Chen J. [1 ]
Jia X. [1 ]
机构：
[1] Shijiazhuang Campus of Army Engineering University, Hebei, Shijiazhuang
来源：
Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 02期
关键词：
equipment groups; genetic algorithm; joint optimization; mission assignment; selective maintenance;
D O I：
10.12382/bgxb.2022.0649
中图分类号：
学科分类号：
摘要：
In order to meet the requirement of combat mission, the selective maintenance and mission assignment of equipment groups are implemented simultaneously, which can effectively improve the overall combat effectiveness of equipment groups. For the independent optimization of selective maintenance decision and mission assignment, a joint optimization model of selective maintenance decision and mission assignment of equipment groups is constructed with the goal of maximizing the mission completion probability, and an environmental coefficient is introduced to represent the influence of the working environment of different sub-missions on the unit state. The mission completion probability of the unit is obtained by solving the Markov model, thus obtaining the completion probability of the submission and the entire mission is obtained. A genetic algorithm based on random keys is used to solve the problem, and the influence of the environmental coefficient on the jointly optimized results is analyzed. The validities of the model and algorithm are verified through an example. The numerical analysis shows that considering the combat mission assignment in the selective maintenance decision can get better results. The model can provide theoretical guidance and technical support for equipment maintenance decision in the battlefield environment. © 2024 China Ordnance Industry Corporation. All rights reserved.
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页码：407 / 416
页数：9
相关论文
共 23 条
  • [1] RICE W F, CASSADY C R, NACHLAS J A., Optimal maintenance plans under limited maintenance time [ C ], Proceedings of the Seventh Industrial Engineering Research Conference, pp. 1-3, (1998)
  • [2] SHAHRAKI A F, YADAV O P, VOGIATZIS C., Selective maintenance optimization for multi-state systems considering stochastically dependent components and stochastic imperfect maintenance actions, Reliability Engineering and System Safety, 196, (2020)
  • [3] XU Q Z, GUO L M, SHI H P, Et al., Selective maintenance problem for series-parallel system under economic dependence, Defence Technology, 12, 5, pp. 388-400, (2016)
  • [4] FAN M F, ZENG Z G, ZIO E, Et al., Modeling dependent competing failure processes with degradation-shock dependence, Reliability Engineering and System Safety, 165, pp. 422-430, (2017)
  • [5] WANG H P, DUAN F H, MA J., Selective maintenance model and its solving algorithm for complex system, Journal of Beijing University of Aeronautics and Astronautics, 46, 12, pp. 2264-2273, (2020)
  • [6] LIU Y, HUANG H Z., Optimal selective maintenance strategy for multi-state systems under imperfect maintenance [ J ], IEEE Transaction on Reliability, 59, 2, pp. 356-367, (2010)
  • [7] WANG S H, ZHANG S X, LI Y, Et al., Research on selective maintenance decision-making method of complex system considering imperfect maintenance, Acta Armamentarii, 39, 6, pp. 1215-1224, (2018)
  • [8] TAMBE P P., Selective maintenance optimization of a multicomponent system based on simulated annealing algorithm, Procedia Computer Science, 200, pp. 1412-1421, (2022)
  • [9] CAO W B, JIA X S, LIU Y, Et al., Selective maintenance optimization for fuzzy multi-state systems, Journal of Intelligent & Fuzzy Systems, 34, 1, pp. 105-121, (2018)
  • [10] DAO C D, ZUO M J., Optimal selective maintenance for multistate systems in variable loading conditions, Reliability Engineering and System Safety, 166, pp. 171-180, (2017)
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