首页|基于优化蚁群算法的物流中心拣货路径优化研究

基于优化蚁群算法的物流中心拣货路径优化研究

扫码查看
在物流中心,合理规划拣货路径可以提升拣货效率,然而在工作人员拣货的过程中,没有对拣货路径进行合理的时间规划,拣货效率低.鉴于此,对蚁群算法进行改进,在此基础上优化拣货路径,以达到减少拣货路径长度的目的.结果显示,改进蚁群算法有效降低了拣货路径长度,降低了 24.49%;改进蚁群算法准确度高,达97.82%;在与遗传算法、传统蚁群算法的对比分析中,改进蚁群算法相比遗传算法路径长度减少了 57.86%,相比传统蚁群算法路径长度减少了 35.21%.实验验证了改进蚁群算法的优越性,说明改进蚁群算法可以有效优化拣货路径,减少拣货路径长度,提升物流中心的拣货效率.
Research on Optimization of Picking Path in Logistics Centers He Kun
In logistics centers,reasonable planning of picking paths can improve picking efficiency.However,in the pick-ing process of staff,there is no time for reasonable planning of picking paths,resulting in low picking efficiency.In view of this,this study improves the ant colony algorithm and optimizes the picking path based on it,in order to reduce the length of the picking path.The research results show that the improved ant colony algorithm effectively reduces the length of picking paths by 24.49%;The improved ant colony algorithm has a high accuracy of 97.82%;In comparison and a-nalysis with genetic algorithm and traditional ant colony algorithm,the path length of genetic algorithm is reduced by 57.86%,and the path length of traditional ant colony algorithm is reduced by 35.21%.The experiment verified the su-periority of the improved ant colony algorithm,indicating that the improved ant colony algorithm can effectively optimize the picking path,reduce the length of the picking path,and improve the picking efficiency of the logistics center.

Ant colony algorithmGenetic algorithmPicking pathLogistics CentrePath length

何堃

展开 >

滁州职业技术学院,安徽滁州 239000

蚁群算法 遗传算法 拣货路径 物流中心 路径长度

2020年度安徽高校人文社会科学研究重点项目2021年滁州职业技术学院人文社会科学研究课题2022年滁州职业技术学院人文社会科学重点研究课题

SK2020A0763YJY-2021-24SKZ-2022-04

2024

贵阳学院学报(自然科学版)
贵阳学院

贵阳学院学报(自然科学版)

影响因子:0.294
ISSN:1673-6125
年,卷(期):2024.19(1)
  • 15