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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper studies an automated warehousing system that uses robots to move racks to multiple workstations, enabling pickers to retrieve products from the racks for order fulfillment. The paper addresses the challenge of considering order and rack sequences simultaneously, while also ensuring workload balance and resolving rack conflicts among the workstations. To solve this problem, a comprehensive mixed integer programming model is formulated, and an adaptive large neighborhood search method is proposed. The proposed approach demonstrates significant improvements in rack movements compared to existing practices, with potential savings of up to 62% in a real-world dataset.