装备群选择性维修决策与任务分配联合优化

doi:10.12382/bgxb.2022.0649

摘要/Abstract

摘要：

为满足作战任务需求,对装备群进行选择性维修的同时进行作战任务分配,可有效提高装备群的整体作战效益。针对选择性维修决策与任务分配独立优化的问题,以最大化任务完成概率为目标,构建装备群选择性维修决策与任务分配联合优化模型,引入环境系数表征不同子任务的工作环境对单元状态的影响;通过求解马尔可夫模型得到单元的任务完成概率,进而得到子任务和整个任务的完成概率;采用基于随机密钥的遗传算法进行求解,分析环境系数对联合优化结果的影响,通过算例验证了模型和算法的有效性。研究结果表明:在选择性维修决策时考虑作战任务分配能够得到更优的结果;该模型可为战场环境下装备维修决策问题提供理论指导和技术支持。

关键词: 装备群, 选择性维修, 任务分配, 联合优化, 遗传算法

Abstract:

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 sub-mission 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.

Key words: equipment groups, selective maintenance, mission assignment, joint optimization, genetic algorithm

中图分类号:

马维宁, 胡起伟, 陈静, 贾希胜. 装备群选择性维修决策与任务分配联合优化[J]. 兵工学报, 2024, 45(2): 407-416.

MA Weining, HU Qiwei, CHEN Jing, JIA Xisheng. Joint Optimization of Selective Maintenance Decision and Mission Assignment for Equipment Groups[J]. Acta Armamentarii, 2024, 45(2): 407-416.

图/表 13

参考文献 23

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序号	α_m	N_m_,1	N_m_,2	K_m	N_m
1	1.4	2	1	2	3
2	1.0	1	2	2	3

序号	α_m	N_m_,1	N_m_,2	K_m	N_m
1	1.4	2	1	2	3
2	1.0	1	2	2	3

序号	单元类型	状态概率分布
序号	单元类型	1	2	3	4
1	1	0.13	0.23	0.30	0.34
2	1	0.10	0.12	0.46	0.32
3	1	0.12	0.05	0.26	0.57
4	2	0.10	0.27	0.27	0.36
5	2	0.32	0.50	0.13	0.05
6	2	0.16	0.30	0.26	0.28

序号	单元类型	状态概率分布
序号	单元类型	1	2	3	4
1	1	0.13	0.23	0.30	0.34
2	1	0.10	0.12	0.46	0.32
3	1	0.12	0.05	0.26	0.57
4	2	0.10	0.27	0.27	0.36
5	2	0.32	0.50	0.13	0.05
6	2	0.16	0.30	0.26	0.28

算法	迭代次数	最优值	平均值	标准差	计算时间/s
枚举法		0.8872			1.2078
	50	0.8872	0.8861	0.0016	0.1104
遗传算法	100	0.8872	0.8865	0.0012	0.2170
	150	0.8872	0.8871	0.0008	0.3301
	200	0.8872	0.8872	0.0000	0.4365