Recently, the collaborative research paper titled “Human-Centric Order Picking: Performance Prediction and Robot Assignment at a Robotic Fulfillment Center”, co-authored by Professor Wu Zhiqiao from the School of Management Science and Engineering and the Key Laboratory of Big Data Management and Optimization Decision-Making of Liaoning Province, and Associate Professor Hao Zhaowei from the Institute of Modern Supply Chain Management, has been formally accepted for publication by Manufacturing & Service Operations Management, a top-tier international journal. Additional co-authors include Professor Jian Luo from Hainan University and Professor Wei Qi from Tsinghua University.

In robotic fulfillment centers empowered by unmanned and intelligent technologies, automated guided vehicles transport movable shelf racks to pickers’ workstations—eliminating the need for workers to travel to retrieve items—thereby enhancing  order-picking efficiency and expanding operational scale. However, due to the variability in human behavior, pickers often become the bottleneck in the streamlined order-picking process. They undertake high-intensity, stationary, and repetitive tasks, which may cause physical and mental health problems.

To address this challenge, the research team proposes an interpretable machine learning prediction model to forecast two critical metrics of picker performance: picking time and performance inconsistency. Building on these predicted metrics, they further design a mixed 0–1 optimization model to improve the assignment between pickers and orders.

The study shows that the proposed model significantly outperforms state-of-the-art forecasting models in prediction accuracy. Based on simulation results using real data from JD.com, the optimization strategy reduces the number of unfulfilled items by 14.2% and increases workers’ average picking efficiency by 7.5%.

Manufacturing & Service Operations Management is the flagship journal in operations management published by the Institute for Operations Research and the Management Sciences (INFORMS). It is included in both the UTD24 and FT50, and is widely recognized as a top-tier international journal in the field of management science.

Written by: Zhao Yongli, Zhu Han

Source: School of Management Science and Engineering, Institute of Modern Supply Chain Management