Order picking optimization in a robotic mobile fulfillment system

• Published in: Expert systems with applications 2022-12, Vol.209, p.118338, Article 118338
• Main Authors: Zhang, Shuanglu, Zhuge, Dan, Tan, Zheyi, Zhen, Lu
• Format: Article
• Language: English
• Subjects: multi-type robot scheduling; pallet integration; robotic mobile fulfillment system; variable neighborhood search
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.118338

•We focus on the order picking process optimization in the RMFS.•An integrated order and robot scheduling optimization problem is studied.•A mixed-integer non-linear programming model is formulated.•Some managerial implications for warehouses managers are proposed. The Robotic Mobile Fulfillment System (RMFS) is a parts-to-picker warehouse system where robots are used to fetch inventory goods from the storage area and transport them to the workstation. To improve the overall performance of the order picking process, this paper investigates an integrated order and robot scheduling optimization problem. We formulate this problem as a mixed-integer non-linear programming (MINLP) model with an objective of minimizing the total cost, containing the transportation cost of robots, the cost of loading and unloading operation, the incentive cost, and the penalty cost. And a Variable Neighborhood Search algorithm is presented to solve the MINLP model. Numerical experiments are conducted on 30 test cases based on real-world data to validate effectiveness of the proposed model and algorithm. Results show that the formulated model contribute to cost saving, and the proposed algorithm not only solves the model within a reasonable CPU time, but also yields near-optimal solutions with about 0.67% relative gap. Moreover, the study find that the reasonable robot configuration has a significant impact on the overall system performance.