Optimization Of Storage Allocation And Replenishment Strategy Of AGV Picking System Based On Order Analysis

The increasingly fierce market competition has caused a fundamental change in the distribution center mode of enterprises.The distribution of orders with a small number of batches and a large number of batches has changed to a small number of batches.This phenomenon is more common in e-commerce enterprises,and the improvement of customer satisfaction has become more challenging for the distribution center.According to the survey,when it is not a holiday period,most of the e-commerce industry will usually require the completion of the entire process from the delivery to the arrival of goods across the country within 48 hours.In this process,the distribution center needs to deal with a large number of items and the quantity of each item,so the distribution center needs to work more efficiently and respond to orders more quickly.Based on the research of AGV picking system,this paper proposes an optimization method for its storage allocation and replenishment strategy,so as to improve the sorting efficiency of AGV picking system.On the distribution of AGV by picking a storage system optimization,first uses the EIQ analysis method in historical order data items of orders and extract the accounts for a larger proportion,for they establish K-means cluster analysis model,according to the result of clustering dividing each item in the store when the bit allocation needs to reduce its storage to picking mouth distance priority,then on this basis for reservoir distribution.It has been proved that the optimized storage allocation in this paper has a shorter journey and fewer shelf handling times in the selection process,which can reduce the time spent by AGV in the selection process,compared with the storage allocation obtained in the literature by using item-order binary network clustering analysis.In order to optimize the replenishment strategy of AGV picking system,a combined forecasting model based on grey model,regression analysis and moving average method is constructed.Compared with the above three methods,the combined prediction model is more accurate and feasible.Therefore,according to the data of historical item demand,the combined prediction model can be used to calculate the forecast demand of each item in thefuture.Then,based on the prediction results,a model of the lowest replenishment cost under the limitation of capacity was constructed.Under the conditions that the maximum working time of the workers and the maximum storage capacity of the warehouse were not exceeded,the replenishment strategy was planned with the lowest total cost of moving the shelves in the product preparation process and replenishment process of the AGV as the target.It has been proved that the optimized replenishment strategy consumes less AGV cost and requires 46%less replenishment time in the replenishment process compared with the traditional replenishment strategy.