Optimization Research On Order Batching And Picking Path In Warehouse Of DC Company

Since the beginning of 2020,COVID-19 has erupted in the world,which has aggravated the shortage contradiction between all kinds of materials.The demand for timeliness of logistics in the society increased rapidly,and that brought considerable challenges to the supply chain.The order picking process in supply chain,which takes the longest time,has been paid more attention.In general,most companies do not pretreat the picking list and start picking once receiving the picking orders,which inevitably leads to repeated walking and time waste during picking process in the warehouse and reduces the efficiency of picking operation.DC company,a well-known transmission and control technology supplier in the industry,has encountered similar problems in the picking process of the warehouse.The volume of daily delivery in the warehouse is large.Although it merges the picking orders according to certain rules and picks by batches,there are still problems exist such as too long picking time.In this background,this thesis takes the warehouse of DC company as the research object,and focuses on the problem of picking process in the middle bin area.Firstly,introduces the research status of warehouse picking strategy and the solutions of picking problem,and raises the research focus of this thesis is to optimize the order batching and picking path.Secondly,through improved genetic K-means clustering algorithm,the initial clustering centers and the number of clusters are optimized.The model is constructed to solve the order batching problem by maximizing the similarity of picking orders.As to the route optimization of the picking list after batching,this thesis takes the minimum total picking distance as the objective to build the model,and introduces genetic simulated annealing algorithm to solve the problem,which improves the limitation of traditional genetic algorithm while solving TSP problem.Through MATLAB programming,the validity of the model is verified by substituting the data of 86 picking orders in the middle bin area on November 2,2020.By comparing the results of the model with the actual picking data of the warehouse,the optimization conclusion of the picking distance is obtained.In this thesis,through the construction of order batching model and picking path optimization model,the problems of repeated picking path and low picking efficiency of order picking in warehouse are solved,which has a certain practical significance.The results show that under the condition of meeting the capacity limit of picking trucks,the optimized picking strategy can reduce the number of batches by 48.38%and the total walking distance by 42.22%compared with the actual picking data in warehouse.The picking problem has been solved,which improves the efficiency of picking process and reduces the occurrence of overtime work.While improving the efficiency,it also reduces the cost of warehouse operation.