• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (10): 1832-1843.

• 人工智能与数据挖掘 • 上一篇    下一篇

低层人工拣货仓库货位优化问题研究

罗嫚玲,林海,刘威   

  1. (武汉大学国家网络安全学院空天信息安全与可信计算教育部重点实验室,湖北 武汉 430000)

  • 收稿日期:2021-01-21 修回日期:2021-06-01 接受日期:2022-10-25 出版日期:2022-10-25 发布日期:2022-10-28
  • 基金资助:
    国家自然科学基金(62072344)

Slotting optimization of low-level manual picking warehouse

LUO Man-ling,LIN Hai,LIU Wei   

  1.  (Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,
     School of Cyber Science and Engineering,Wuhan University,Wuhan 430000,China)
  • Received:2021-01-21 Revised:2021-06-01 Accepted:2022-10-25 Online:2022-10-25 Published:2022-10-28

摘要: 在现代物流总成本中,仓储成本占很大比重,合理的储位分配是提高仓储拣选效率,降低仓储成本的核心所在。通过对低层人工拣货仓库的出库过程分析,同时考虑商品热销程度、商品之间的关联关系及货架位置等影响因素,设计了基于社区划分的货位优化算法。首先,根据商品关联性构建无向有权网络,并采用社区划分算法进行多次划分;然后,以社区为单位存放到货架,并通过调整阶段补齐货架;最后根据评估指标从多个方案中选出最优方案。评估指标根据缩短行走路径、缓解堵塞和减少拣选人员数量3个优化目标构建。实验结果表明,提出的货位优化算法无论是时间消耗还是货位摆放方案质量,与其他对比方案相比均具有显著优势。

关键词: 低层人工拣货仓库, 货位优化, 社区划分算法, 货位优化算法, 评估指标

Abstract: Warehousing costs account for a large proportion of the total cost of modern logistics. Reasonably slotting optimization is the core of improving the efficiency of warehouse picking and reducing costs. By analyzing the outbound process of low-level manual picking warehouses and considering in-fluencing factors such as the degree of product hot-selling, the relationship between the products, and the location of shelf, a slotting optimization algorithm based on the community division algorithm is designed. The algorithm firstly builds undirected weighted networks based on the product relevance, and then uses a community division algorithm to divide it multiple times; Secondly, it is stored on the shelf in the community as a unit, and the shelf is filled through the adjustment phase. Finally the optimal product placement is selected from multiple placements based on evaluation indicators. The evaluation index is established based on the three optimization goals of shortening the walking path, alleviating congestion and reducing the number of pickers. Experiments show that the proposed slotting optimization algorithm has significant advantages compared with other comparative solutions in terms of time consumption and the quality of the product placement.


Key words: low-level manual picking warehouse, slotting optimization, community division algorithm, slotting optimization algorithm, evaluation indicator