Order Picking: Exploring the Properties of the Greedy Seed-Based Batching Algorithm

Order picking is one of the most relevant optimization problems in the context of warehouse optimization. Especially within an e-commerce environment, order picking activities depend largely on the partitioning of individual customer orders into groups of orders that will be picked within a single pick tour, i.e., the order batching problem. The goal of this paper is to examine the optimization choices of the greedy seed-based batching algorithm. This algorithm follows a constructive, myopic approach in which batches are created consecutively by adding orders to the partial batch. It is widely used in different forms, but the reasons for its performance and the quality of its solution have not yet been addressed in the literature. We present a simulation study to investigate the properties of the individual pick tours that result from applying the seed-based batching algorithm. More specifically, we assess the optimality of the batching algorithm's myopic choices by comparing the myopic picking cost of orders that were the second best choice with their actual picking costs. Furthermore, we use this solution as a starting point for the variable neighborhood search algorithm to compare the cost of the myopic solutions. The results show that the decisions related to the choices of seed order, the constructive criterion to select orders to batch, and the storage policies all play major roles in the performance of the greedy seed-based algorithm.