Equipment maintenance task prediction model analysis based on BAS-PSO hybrid optimization algorithm

被引:2
|
作者
Song, Weixing [1 ]
Wu, Jingjing [2 ]
Chang, Huiqiang [3 ]
Xu, Guihua [4 ]
机构
[1] Army Engn Univ PLA, Shijiazhuang, Hebei, Peoples R China
[2] Western Theater Army Dept Logist, Lanzhou, Peoples R China
[3] Unite 31682 PLA, Lanzhou, Peoples R China
[4] Unite 32272 PLA, Lanzhou, Peoples R China
关键词
Maintenance tasks; motor hours; balance of payments; echelon storage; equipment utilization; beetle antennae search algorithm; particle swarm optimization algorithm;
D O I
10.1080/21642583.2021.1930276
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the maintenance task prediction problem of armored forces, the macro model and micro model are established to analyze the constraint conditions, and the equipment maintenance task prediction model is established in order to meet the motor hours echelon storage. Under the condition of meeting the balance of annual motor hours payments, the motor hours consumed by equipment are allocated according to the annual training tasks, and a hybrid optimization algorithm of improved particle swarm optimization is designed to solve the model, and a case study is carried out on a few vehicles in a certain army. The simulation results show that the model can effectively solve the problem of equipment maintenance task prediction, and a provide reference value for troops to make the maintenance plans.
引用
收藏
页码:455 / 466
页数:12
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