摘要
研究了多AGV多目标任务调度的数学模型,考虑了自主移动小车的可变速度和载重量,对完成任务的最大时间、自主移动小车的数量、以及小车用电量3个目标进行优化,结合一种改进的自适应遗传算法(IAGA),通过对初始种群和自适应策略的优化,提高算法的收敛速度。最后,经过算例分析,得到相应任务调度结果的甘特图以及算法的收敛曲线,实验表明,改进的自适应遗传算法相对于传统的遗传算法,在合适的速度下,使用了较少的AGV可以完成所有任务,不仅缩短了最大完工时间,也减少了AGV的使用数量,降低了总功耗。
This studies the mathematical model of multi-AGV multi-objective task scheduling,considering the variable speed and load weight of the autonomous mobile trolley,the maximum time,the number of autonomous mobile trucks,and three targets of the vehicle electricity,and optimizeCombined with an improved adaptive genetic algorithm(IAGA),the convergence speed of the algorithm is improved by the optimization of the initial population and adaptive strategy.Finally,after an example analysis,the Gantt chart and the convergence curve of the corresponding task scheduling result,the experiment showed that the improved adaptive genetic algorithm relative to the traditional genetic algorithm,using fewer AGVs at the right speedAll tasks can be completed,not only shortened the maximum completion time,but also reduced the number of uses of AGV,which reduces total power consumption.
作者
张彦如
陈灏
石珂
ZHANG Yan-ru;CHEN Hao;SHI Ke(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)
出处
《组合机床与自动化加工技术》
北大核心
2022年第10期159-163,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金资助项(62173119)。
关键词
可变速度
多目标
任务调度
改进自适应遗传算法
autonomous mobile vehicle
multi-objective
task scheduling
adaptive genetic algorithm
