基于聚类分析的盘点分批还货研究

INVENTORY-BASED REPAYMENT RESEARCH BASED ON CLUSTER ANALYSIS

电商环境下,订单量大,每笔订单所包含的商品数量少、种类多,且要求仓配中心迅速做出响应,将盘点作业中发现的差异商品及时归还到源库位上是保证订单正常发运的重要作业。本文以Q公司大型仓配中心为例,针对盘点作业中的还货问题,应用订单分批原理,将各差异商品基于巷道相似系数进行聚类分析,采用分批还货的作业方式,经FlexSim仿真软件的仿真验证可知,分批还货在还货时间以及还货路径上都优于现行还货方式,提高了还货作业的效率。

Under the e-commerce environment,the order quantity is large,and the quantity of goods included in each order is small and varied,and the warehouse distribution center is required to respond quickly,and the difference goods found in the inventory operation are returned to the source location in time.It is an important job to ensure the normal delivery of orders.This article takes Q company's large warehouse distribution center as an example.For the problem of returning goods in the stocktaking,applying the principle of order batching,clustering analysis of different differential goods based on lane similarity coefficient,using batch returning operation mode,FlexSim The simulation verification of the simulation software shows that the batch returning goods are superior to the current returning method in the returning time and the returning path,which improves the efficiency of the returning operation.