摘要
针对运输机物资投送的任务分配以及路径规划问题,重点考虑了由恶意干扰导致的运输机损耗以及投送时效性两个复杂性因素,建立了包含运输机损失、总里程数以及未按时受补率三方面的待优化目标函数。同时提出了基于“任务合并”和“饱和补给”等先验知识的优化思路,实现了改进的遗传算法。仿真结果表明,采用融合先验知识的改进遗传算法,改善了因搜索空间过大而导致的优化过程收敛缓慢问题,提升了模型的解算速度和优化效果。
This paper focuses on the task allocation and path planning problem for aircraft material delivery,considering two complexity factors:aircraft loss caused by malicious interference and delivery timeliness.It establishes an objective function to be optimized,which includes aircraft loss,total mileage,and untimely supply rate.Meanwhile,it proposes an optimization approach based on prior knowledge such as"task merging"and"saturation supply",and implements an improved genetic algorithm.Simulation results show that the improved genetic algorithm with fused prior knowledge addresses the slow convergence problem caused by a large search space,improving the solution speed and optimization effect of the model.
作者
张雷
安靖
陈亮
ZHANG Lei;AN Jing;CHEN Liang(Joint Operations College of National Defence University,Beijing 100000,China;Joint Logistics College of National Defence University,Beijing 100858,China;Automobile NCO Academy,Army Military Transportation University of PLA,Bengbu 233011,China)
出处
《指挥控制与仿真》
2024年第5期21-28,共8页
Command Control & Simulation
基金
全军军事类研究生资助课题。
关键词
多运输机
战场物资补给
路径规划
任务分配
遗传算法
multi-aircraft
battlefield material supply
path planning
task allocation
genetic algorithm
作者简介
张雷(1986-),女,博士研究生,讲师,研究方向为军事运筹学;安靖(1981-),女,博士,副教授。
