The Study Of Slotting Optimization Based On Rack-to-picker Mode

As the automation and intelligence degree of storage equipment is improved,the rack-to-picker mode is developing to the main patterns of distribution.The picking mode based on the movable shelves saves the labor by quickly picking out the goods through automated guide vehicle(AGV)handling and manual picking.In the logistics distribution center,order picking is the most important process,which accounts for more than 40% of the total operating time,accounting for more than 65% of the total operating cost.Therefore,a reasonable order picking operations can improve the efficiency of distribution.As the basis for order picking,slotting optimization is a common method to improving the efficiency of warehouse operations by assign the goods to the appropriate place.Therefore,this paper takes the rack-to-picker mode as the research object,for the problem of maximize the stock keeping units(SKUs)correlation within the same shelf and ignored the shelf correlation,this paper presents a method to optimize the cargo space according to the operating characteristics of AGV,and through this way to improve the sorting efficiency.The main contents and achievements of this paper as follows:First,analysis the characteristics of rack-to-picker mode,according to the order frequency and correlation of SKUs to cluster,divided the SKUs by the storage number of the shelf.In this way,the SKUs with high order frequency and strong correlation are classified as a cluster.In order to assign the SKUs cluster to an appropriate shelves,the optimization model with minimum picking distance target in the traditional warehouse(with / without cross aisle)and fishbone warehouse is established.Second,according to the operating characteristics of AGV,the cube-per-order index(COI)method is used to solve the problem of slotting optimization.Design the tabu search(TS)algorithm to solve the mathematical model,this algorithm change the position of shelf by random.Based on the TS algorithm,the tabu search considering shelf correlation(TS_SC)algorithm is proposed,TS_SC algorithm through the location exchange strategy to change the shelf position,and reduce the picking distance of AGV.Finally,analyze the model and algorithm through an example.Computer generated a number of orders,cluster the SKUs according to order information,and application of TS algorithm and TS_SC algorithm to solve the mathematical model to test the performance of the method.The test results show that the TS_SC algorithm has better convergence speed and optimization ability than the TS algorithm.Design several groups of experiments with different order quantity and shelf storage bits,comparison of the number of shelf handling times and picking distance of COI method and TS_SC method.The experimental results show that the TS_SC method is more effective.In three warehouses,design some slotting optimization experiments with different SKUs quantity.The results show that the fishbone warehouse has higher area utilization and picking efficiency than the traditional warehouse.