Storage Assignment And Order Batching Optimization Based On Association Rule

As consumers demand more and more on online shopping delivery time,optimizing manual order picking operations to reduce order completion time has become the focus and difficulty of warehouse management for most e-commerce enterprise.The various parts of the e-commerce warehousing operation interact with each other.Storage assignment determines the storage location of the goods to be picked,order batching determines the combination of the order and goods to be picked,so they determine the picking route in a picking task together,which are key of order picking optimization.However,in the actual operation process,e,commerce companies often carry out each operation according to independent criteria,ignoring the relevance of storage assignment and order batching in affecting the picking distance.Previous studies only focus on one activity to optimize order picking.No scholars have considered the mutual influence between storage assignment and order batching and jointly optimize them.At the same time,as the volume of orders to be processed by e-commerce warehousing continues to grow,it is more meaningful to use data mining methods to analyze large amounts of historical order data and to mine potential relationships for optimization.Therefore,this paper adopts the association rule mining algorithm,which focuses on the correlation of customer demands,and jointly optimizes storage assignment and order batching based on unified association rules to improve the efficiency of manual order picking in e-commerce warehouses.This paper analyzes manual order picking operation in e-commerce warehouse and finds that storage assignment and order batching are two key directions for order picking operation optimization.They have relevance in determining the picking distance and product correlation in different scenarios is the important factor to consider for both of them.Therefore,this paper proposes a three-step joint optimization process of storage assignment and order batching through commodity association rules:the two-stage storage assignment model is generated with the goal of minimum total picking distance;the order batching models is constructed with the maximum order correlation within similar time period of all batches as the goal.The storage assignment model reduces the total picking distance by storing the related goods together and the goods with high demand frequency by export.The order batching model constructs the order association rule based on the commodity association rule,and reduces the total picking distance by placing the orders with most association together.Subsequently,this paper uses the FP-growth association rule mining algorithm to construct two storage assignment algorithms based on the commodity association rules and two order batching algorithms based on the order association rules.Finally,this Paper uses Python programming language to solve the model.To analyze the joint optimization effect of storage assignment and order batching based on association rule,the results are compared with random storage,ABC class-based storage,FCFS,and DDB strategies.To explore the influence of different factors on the optimization effect of storage assignment and order batching,sensitivity analysis is carried out by changing the association rule mining threshold,number of commodity types,order quantity,picking equipment capacity.The overall research results show that storage assignment and order batching based on association rule have achieved good optimization effect on reducing the total picking distance and improving picking efficiency.The joint optimization can reduce the total picking distance by over 12.9%.The research in this paper can provide a new method for e-commerce enterprises to optimize order picking efficiency,and it is also an extension and innovation of order picking research.There are 29 figures,20 tables and 57 references in this paper.