E-commerce order batching algorithm based on association rule mining in the era of big data

In E-commerce environment, consumers have higher requirement on rapid order processing, therefore, how to optimize the efficiency of picking operation in order to shorten the order processing time is the emphasis and difficulty for warehouse management of E-commerce enterprises. E-commerce orders are in small batch and high frequency, therefore, the order batching strategy is more suitable to optimize the picking operation efficiency. In the era of big data, the E-commerce enterprises need to face massive customer orders, which makes it necessary for the E-commerce enterprises to adopt big data analysis methods such as data mining to process orders and design the batching algorithm. In this paper, massive E-commerce order data is processed based on the Association Rule Mining Algorithm FP-growth ,the correlation degree of orders is established and order batching algorithm FPGB is designed. The results of the experiment demonstrate the effectiveness of FPGB. By adopting FPGB, order batching can be completed in seconds and order batching time can be largely enhanced, which realizes the optimization process of E-commerce order batching strategy and warehouse picking operation in the era of big data.