Study On Order Batching Strategy Optimization Under E-commerce Synchronized Zone Order Picking System

Order picking, retrieving items from their storage in order to satisfy customer orders, is one of the most labor-intensive processes that determine warehouse performance to a large part. Up to55%cost can be attributed to order picking. With the rapid development of e-retailer, the order quantity per day is likely to be tens of thousands, which increases the difficulty of order picking. The diversification of storage goods makes the area of the warehouse much larger, and simultaneously augments the walking distance. To improve the picking efficiency and ensure the packages delivery to the customers earlier, we focus on the e-commerce order picking system problem. In this paper, we use the order batching strategy and synchronized zoning strategy simultaneously to improve the order picking efficiency. The order batching polices can be divided into five kinds:priority rule-based algorithms, seed algorithms, saving algorithms, meta-heuristic algorithms and data mining approaches. As the seed algorithms taking no account of the global order similarities, the aisle similarity coefficient is proposed to measure the proximities between two orders. According to the coefficient, we present a clustering criterion to solve the order batching model. The researchers about zoning order picking fouces on the effects of the zone shape, the number of items on the pick-list and the storage policy. Someone analyses the importance of zone workload balance and tackle it based on the commodity distribution. In this paper, we propose an order batching model by minimizing the throuphput time and balancing the workload in each zone. The model is solved by multi-objective generation algorithm. The main contents of this paper are as follows:(1) From the perspective of e-commerce logistics, we analyize the important role and conclude possible issues of order picking system, including order picking system design, warehouse layout; order picking process; order picking route. Current common order picking route strategies are concluded.(2) We define the traditional order batching problem and introduce general order baching model by minimizing the traveling distance. A new similarity clustering approach is established to minimize the pickers’travel distance. The aisle similarity coefficient is proposed to measure the proximities between two orders. According to the coefficient, we present a clustering criterion to solve the order batching model. We compare the clustering approach with the Seed and FCFS algorithms under small, medium, large and mixed order environments respectively. Four different indexes are used to evaluate these three algorithms.(3) We analyze the characteristic of synchronized zone order picking system and operation process of it. Based on the order batching problem of synchronized zone order picking system, we first analyze the total throughput time and workload in each zone, establish the time sequence model of the synchronized zone order picking system, then considering the importance of workload balance in each zone, we establish the multi-objective order batching model by minimizing the throughput time and balancing the workload in each zone. The model is solved by multi-objective generation algorithm. To maintain the diversity of population and control the population evolves towards the direction of the optimal solution, the fitness value of the chromosome is calculated by sorting performance matrix. The self-adaptive crossover probability and mutation probability are developed baded on the total performance of population. An experiment is proposed to prove the feasibility of the model, especially in the situation of small and middle scale order.This paper focuses on the order batching problem of e-commerce synchronized zone order picking system. By taking into accout of similarity clustering approach and fair feeling of pickers, we can improve the efficiency of e-commerce order picking and the work enthusiasm of pickers. This study can provide decision support for warehouse management department and provide guide thinking for more humanized management way of warehouse distribution center.