基于改进K-Means聚类电商物流仓储拣选优化策略  

Optimizing Strategy For Warehousing and Picking of E-commerce Logistics Based on Improved K-Means Clustering

作　　者：朱友琼 唐思 ZHU You-qiong;TANG Si(School of Economics and Management,Southwest University of Science and Technology,Mianyang 621010,China)

机构地区：[1]西南科技大学经济管理学院

出　　处：《物流工程与管理》2019年第7期77-79,共3页Logistics Engineering and Management

摘　　要：拣选作业是电商物流仓储作业中最核心的部分。由于电子商务环境下的订单具有多品种、少批量,且需求时间随机的特点,单纯按照先到先服务的拣选策略不仅难以保障订单履行的时效性,还会造成拣选效率低下,拣选成本居高不下等问题。文中基于节约算法的思想对K-Means聚类算法进行改进,以减少移动货架搬运次数为目标函数,计算两两拣选波次的耦合度,根据耦合度的大小对波次单进行分类,然后运用K-Means聚类算法建立模型并求解,得出波次单的拣选顺序。最后通过系统仿真验证,证实此方法能够有效优化拣选顺序,并提升拣选效率。Picking operations are the core part of e-commerce logistics and warehousing operations.Due to the variety of orders,low volume,and random demand in the e-commerce environment,the pick-and-go strategy based on first-come-first-served service is not only difficult to guarantee the timeliness of order fulfillment,but also results in poor picking efficiency and high picking costs.In this paper,based on the idea of saving algorithm,the K-Means clustering algorithm is improved to reduce the number of moving racks as the objective function,calculate the coupling degree of the two pairs of picking waves,classify the wave order according to the degree of coupling,and then apply The K-Means clustering algorithm establishes the model and solves it,and obtains the picking order of the wave order.Finally,the system simulation proves that this method can effectively optimize the picking order and improve the picking efficiency.

关 键 词：K-MEANS聚类 节约算法 订单排序优化 

分 类 号：F250[经济管理—国民经济]