Optimizing Order Batching and Picking Problems Considering the Correlation Between Products Under the Scattered Storage Mode

Abstract

With the rapid development of e-commerce, the scattered storage mode has been widely applied in B2C distribution centers in which there is a large assortment and quantity of small-sized, time-sensitive orders. Under the scattered storage mode, obtaining high-quality batching results and quickly completing order picking are key to improving the operation efficiency of a distribution center when a large number of orders arrive in a short period. Against this background, a new order batching problem under the scattered storage mode is studied. The feature is to improve the batching quality by considering the correlation between products. The problem is formulated as a 0–1 integer programming model to maximize the sum of pair-to-pair order correlations in all batches. To solve large-scale problems, we first propose two new seed batching algorithms based on the correlation between products. The first one selects the order with the largest number of products as the seed order, and the second one selects the order with the highest correlation as the seed order. Then tabu search (TS) is used to improve these two algorithms. In addition, a new seed batching algorithm for a special situation is proposed, which needs to use the location information of each product to obtain more accurate batching results. Finally, an improved two-stage order picking algorithm is proposed to verify the actual picking effect of the batching results obtained from the different algorithms. The experimental results show that the two seed batching algorithms improved by TS are superior to the existing batching algorithms in batch quality for the general situation, and the second seed batching algorithm improved by TS performs better for large-scale problems. Moreover, the new seed batching algorithm is more efficient and effective.