Stronger mixed-integer programming-formulations for order- and rack-sequencing in robotic mobile fulfillment systems

This paper addresses the order-and rack-sequencing problem at a single picking station in the context of robotic mobile fulfillment systems, a warehouse technology typically applied in large distribution centers. Following the parts-to-picker concept, items are stored on movable racks that are lifted and transported by automated guided vehicles from the storage area to picking stations for order-processing. The order-picking process involves two linked decisions: How to sequence the processing of orders and how to se-quence the rack visits to supply the picking station with the requested items. We present a novel mixed-integer linear programming formulation achieving stronger linear programming bounds than a previous formulation. Including preprocessing techniques it quickly solves instances of medium-size to proven op-timality for the first time in literature. For large real-world instances, we provide a three-stage heuristic solution procedure suitable in a dynamic environment, while providing competitive solutions within a short run time. Computational experiments on a broad set of benchmark instances and a comparative study with approaches from literature verify our results.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper focuses on the order-and rack-sequencing problem in robotic mobile fulfillment systems. It presents a novel linear programming formulation and preprocessing techniques to solve medium-sized instances quickly and prove optimality. For large real-world instances, a three-stage heuristic solution procedure is provided, which delivers competitive solutions within a short run time. Computational experiments and a comparative study verify the effectiveness of the proposed approach.