基于改进Apriori算法的仓库货物关联度分析

Analysis of warehouse goods association degree based on improved Apriori algorithms

在物流仓储业迅猛发展的背景下,仓储业存在着许多问题亟待解决.仓储企业为了缩短拣货路径与时间,增强拣货操作效率,优化货位分配过程,针对仓储业中仓库布局不合理、货位分配不恰当导致仓库运作效率低下、拣货时间冗长等问题,利用Apriori算法对仓库货物品种的关联性进行分析,并在传统Apriori算法的基础上对其搜索项进行简化进而提高算法的效率形成改进的Apriori算法,分析改进Apriori算法与传统Apriori算法在性能上的提高.以某卷烟配送中心Q仓库为例,使用改进的Apriori算法研究其仓库货物品种的关联度,结果显示改进的Apriori算法运行时间要低于传统Apriori算法.该成果可利用到实际的仓库货位优化仓操作中,根据算法获取的关联度指导仓库的货位分配,缩短拣货时间,增强拣货效率,提高仓库的经济效益.

In the context of the rapid development of logistics and storage industry,there are many problems to be solved.In order to shorten the path and time of picking,enhance the efficiency of picking operation,and optimize the process of location allocation,in view of the problems such as unreasonable warehouse layout,improper location allocation,resulting in low efficiency of warehouse operation,long picking time and so on,the storage enterprises use Apriori algorithm to analyze the relevance of warehouse goods varieties,and on the basis of traditional Apriori algorithm to search items to simplify and improve the efficiency of the algorithm to form the improved Apriori algorithm,analyzed the performance improvement of the improved Apriori algorithm and the traditional Apriori algorithm.Finally,taking a Q cigarette distribution center warehouse as an example,used the improved Apriori algorithm to study the correlation degree of the goods in the warehouse.The results showed that the running time of the improved Apriori algorithm was lower than the traditional Apriori algorithm.Therefore,this achievement can be used in the actual warehouse location optimization operation.According to the correlation degree obtained by the algorithm,it can guide the location allocation of the warehouse,shorten the picking time,enhance the picking efficiency and improve the economic benefits of the warehouse.