Online joint optimization of order picking process in robotic mobile fulfillment systems

Abstract

A robotic mobile fulfillment system (RMFS) constitutes a highly intricate and interdependent decision-making system, encompassing numerous closely related and interacting decision challenges. Meanwhile, RMFS is a prime example of a dynamic system in which various types of information, such as inventory levels, order backlogs, robot statuses, and pod locations, continuously change throughout operation. This study focuses on the online decision-making process for joint optimization of order assignment, pod selection, and task allocation within RMFS during the order-picking process. Additionally, it considers the influence of current decisions on future decisions to improve online decision-making efficiency. Initially, a dynamic system model for RMFS is developed. Subsequently, a novel mixed-integer linear programming (MILP) model is formulated to address this critical order-picking challenge effectively. Furthermore, a customized heuristic approach, employing alternating decision-making and greedy selection techniques, is proposed to overcome computational challenges associated with larger-scale RMFS scenarios. In different scales, the proposed approach outperforms a sequential approach commonly used in practice and a MILP model in existing literature for online joint optimization of order assignment and pod selection. The findings highlight the efficacy of the proposed approach in optimizing the RMFS order-picking process, particularly for larger instances, delivering significant benefits in terms of increased throughput and reduced robot travel distances.