Batch Assorting for Worker-Following Assortment Carts in Parallel-Aisle Order-Assorting Systems

Abstract

This study introduces an order-assorting system (OAS) in a distribution center. The system supports assortments with worker-following carts. The workers and worker-following carts move during an order-assortment operation before which the binning operation splits the large-volume stock-keeping units (SKUs) into bins according to the number of aisles. We propose two mixed-integer programming models. The batching-only model (BOM) conducts the batching operation to shorten the total travel distance. The binning and batching model (BBM) assumes that all SKUs are split into bins according to the number of aisles and finds the optimal point between binning and batching. We also propose the route packing-based binning then batching (RPBB) heuristic to solve a large-sized BBM problem. RPBB consists of a binning procedure based on route packing (BPM-RP) and a batching procedure using a simple integer programming formulation. The results of the experiments evaluating the performance of the BBM and the RPBB heuristic show that the model and heuristic optimize the balance between binning and batching to reduce the total travel distance. In the large-sized problem, the RPBB obtains near-optimal solutions by the tight lower bound that shows 1.41-2.30% optimal gaps on average.