指挥控制与仿真2026，Vol.48Issue(2)：140-147,8.DOI:10.3969/j.issn.1673-3819.2026.02.020
基于任务聚类的维修力量编组优化模型与改进NSGA-Ⅱ求解算法
Mission-based clustering optimization model for maintenance force grouping and improved NSGA-Ⅱ solution algorithm
摘要
Abstract
In response to the current issues with the organization of wartime equipment maintenance forces,a force organiza-tion method based on mission clustering analysis and an improved NSGA-Ⅱ algorithm is proposed.Based on the mission clustering analysis results obtained from the maintenance task clustering model,a multi-objective optimization model for ma-intenance force grouping was established with the objectives of minimizing the total maintenance time and the standard devia-tion of personnel workload.For the solution method of this multi-objective optimization model,the elite retention strategy and crossover operator of the traditional NSGA-Ⅱ algorithm were optimized and improved.The improved NSGA-Ⅱ algorithm was verified through the ZDT test function in terms of its superiority in convergence and solution set distribution.Using the main-tenance mission of a certain artillery group as an example,simulation experiments and model algorithm analysis were conduc-ted,resulting in a set of relatively ideal maintenance force organization schemes.This provides methodological and technical support for decision-makers to select schemes based on battlefield requirements and preference differences.关键词
维修力量编组/任务聚类/基本维修单元/改进NSGA-Ⅱ算法Key words
maintenance force formation/mission clustering/basic maintenance unit/improved NSGA-Ⅱ algorithm分类
军事科技引用本文复制引用
杨亮亮,赵德勇,刘晓勇..基于任务聚类的维修力量编组优化模型与改进NSGA-Ⅱ求解算法[J].指挥控制与仿真,2026,48(2):140-147,8.基金项目
陆军工程大学军事理论创新工程项目(KYSZJKQTZL24005) （KYSZJKQTZL24005）
