Research On Order Picking Efficiency Optimization Of E-commerce Distribution Center Based On Demand

With the rise of e-commerce and online retail,orders in distribution centers are characterized by small batches and high frequency.In order to improve customer satisfaction,companies must sort out orders in a short period of time.Therefore,Optimizing the order sorting system to reduce order response time has become the focus of most e-commerce companies.In the operation of the warehouse,every link of the order picking operation will affect the work efficiency of the picking staff.Select a reasonable order picking system,and improve the efficiency of the entire picking operation through a suitable location allocation strategy and order batching strategy.For e-commerce enterprise distribution centers is of great significance.This article takes the order sorting system of the distribution center in the e-commerce environment as the research object.By combing the various processes of the order picking operation,it is found that the location allocation and order batching can greatly improve the efficiency of order sorting.Problem optimization.Aiming at the problem of location allocation,combined with demand correlation and order frequency,a K-Means clustering algorithm based on demand correlation is proposed.The algorithm is divided into three stages: the first stage is to assign items to different lanes.The second stage is to assign items to different shelf levels,and the third stage is to assign items to different locations.In order to solve the problem of order batching,based on the correlation between customer historical orders,an order batching model based on association rules is proposed,and the order set is divided into related orders and non-related orders according to the correlation between orders.,According to the maximum support for batching,the two orders with the most support as seed orders,and then select the order with the most relevant order in the batch into the same batch;for non-associated order set,calculate all The walking distance of the order,with the minimum walking distance as the seed order,and then select the order with the smallest sum of the walking distance of the orders in the batch into the same batch.Finally,it is compared with the random allocation strategy,non-batch strategy,and first-come-first-served strategy to verify the effectiveness of the proposed algorithm.The research results show that the K-Means clustering algorithm based on demand correlation and the order batching model based on association rules can effectively reduce the number of traversed channels and the number of order batches,and then significantly reduce the walking distance of pickers.The K-Means clustering algorithm based on demand correlation can reduce the walking distance by 16.71%,the order batch model based on association rules can reduce the walking distance by17.8%,and the joint optimization strategy can reduce the walking distance by 25.1%.The related research in this article provides a new idea for the order picking operation of the e-commerce distribution center,and has certain guiding significance for the actual production activities.