Research On Integrated Scheduling Of Order Batching And Delivery Of B2C Distribution Center

With the expansion of information technology,e-commerce occupies an increasingly important role in modern business.Under the background of the rapid development of e-commerce mode and the increasing demand for timeliness of ecommerce shopping,how to improve the efficiency of customer order fulfillment in ecommerce distribution centers is an urgent problem to be solved in e-commerce logistics management.The uncertainty such as the category,quantity and customer location of e-commerce orders makes it more difficult to optimize the order fulfillment time.Based on this,how to coordinate the order picking and delivering of e-commerce distribution centers is the key to ensure that customers can receive their commodities as soon as possible and the order services were fully completed.However,most of the current scholars focus on the single phase such as e-commerce order batching or vehicle routing.Considering that the single optimization of order batching or delivery can only get the optimum of single phase and cannot achieve the overall optimum,this dissertation focuses on the joint scheduling problem of order batching and distribution in e-commerce distribution centers based on the overall efficiency of picking and delivering.This dissertation regards the integrated scheduling order batching and distribution problem as a special integrated scheduling production and distribution problem.Under the circumstance that picking path is S-shape,the location distribution and customer demand are known,and the number of vehicles is infinite,a mathematical model with vehicle capacity and loading rate constraints is proposed to minimize the time from the order picking to delivering of the e-commerce distribution centers.Based on the conclusion of the integrated scheduling production and distribution problem,we can see that the problem studied in this dissertation is a NP-hard problem and it cannot be solved by accurate algorithm.In order to solve this model,the model is divided into different processes,and a four stage heuristic algorithm is designed:(1)Optimization of region segmentation.The demand location is segmented into different regions by K-means clustering algorithm and the clustering results is evaluated by the cluster validity index.Then,the optimal segmentation scheme can be found by the evaluation results and the regions are adjusted according to the capacity and loading rate constraints.(2)Optimization of vehicle routing.The ant colony algorithm is selected as the solution of routing optimization problem by comparing the performance of various solutions.At the same time,in order to further optimize the efficiency of the algorithm,this dissertation proposes that using the fuzzy set theory to improve the ant colony algorithm(Fuzzy Ant Colony Optimization algorithm),and the effectiveness of this algorithm is verified with case study.The improved algorithm is used to optimize the vehicle routing of each region after the segmentation.(3)Optimization of order batching.Based on similarity clustering rule,a mathematical model of batching optimization is established for the orders contained in each distribution path,and the improved seed algorithm based on seed order is applied to solve the model.(4)Optimization of picking sequence.The picking sequence is adjusted based on the result of routing optimization to ensure that the execution time of all orders is minimized.Finally,the effectiveness of the model and the algorithm is verified by data experiments,and the results proved that the joint scheduling optimization which considering order batching and delivering is better than the optimization of order batching or delivering independently.