Order picking optimization with rack-moving mobile robots and multiple workstations

In this paper, we study an automated warehousing system, where racks are moved by robots to multiple workstations so that pickers at each workstation can retrieve the products from the racks to fill up the orders. In this context, the order and rack sequences should be considered simultaneously and the workload balance and rack conflicts among multiple workstations should also be taken into considerations. However, these factors have not been addressed in the current literature. To fill this gap, we formulate a comprehensive multi-workstation order and rack sequencing problem as a mixed integer programming model that accounts for workload balancing and rack conflicts. To solve the model, we propose an adaptive large neighborhood search method, which builds on a newly developed data-driven heuristic that exploits the structure of the problem and simulated annealing. We show that our proposed approach performs well on both small-scale problem instances with synthetic data and a large-scale real-world dataset supplied by a large e-commerce company. In the latter case, it can save up to 62% in rack movements compared to the company’s current practice.