摘要
订单分批问题是目前关于优化拣选作业研究的重点,基于此,根据单区型仓库的特点,建立了订单分批模型,并将基于"反学习"理论的人工蜂群算法应用于订单分批问题中,将分批优化后的结果分别同基于通道相似性分批算法和基于图论的分批算法进行比较,结果表明,利用该算法在拣选距离上相较于前两种算法分别缩短了35.9%和33.5%,该算法同其他分批算法相比具有较为明显的优势,未来利用该算法处理订单分批问题具有一定的推广意义。
In this paper, according to the characteristics of a single-zone warehouse, we established the order batching model of the warehouse, applied the artificial bee colony algorithm based on the "reverse learning" theory to the problem, the result of which was respectively compared to that derived based on the channel similarity based batching algorithm and the graph theory based batching algorithm to find that the reverse learning based algorithm could reduce the picking distance by 35.9% and 33.5% respectively compared to the two algorithms.
出处
《物流技术》
2017年第12期90-94,共5页
Logistics Technology
基金
北京市属高等学校青年拔尖人才培育计划项目(CIT&TCD201504052)
北京物资学院青年运河学者资助项目
北京物资学院国家级科研项目培训基金项目
关键词
订单分批
人工蜂群算法
“反学习”理论
order batching
artificial bee colony algorithm
"reverse learning" theory
