Robot Scheduling for Mobile-Rack Warehouses: Human-Robot Coordinated Order Picking Systems
王征，大连海事大学教授，博士生导师，美国亚利桑那大学访问学者，多年来致力于智慧物流与供应链管理、运筹与优化、大数据分析与处理、运营管理与智能决策等领域的相关研究，在Production and Operations Management、Transportation Research Part B/C/E、Annals of Operations Research、Computers & Operations Research及系统工程理论与实践、中国管理科学等本领域知名期刊发表论文七十余篇，出版专著2部，担任Transportation Research Part E、Journal of Advanced Transportation等期刊特刊编委，中国系统工程学会智能制造系统工程专业委员会委员、中国优选法统筹法与经济数学研究会高等教育管理分会副秘书长，主持国家自然科学基金、辽宁省重点研发计划、大连市重点学科重大课题等项目20项，指导学生获国家和省部级竞赛奖19人次，荣获2022年中国物流与采购联合会科技创新人物，主持完成的研究成果获中国物流与采购联合会科技进步一等奖、中国商业联合会科技进步一等奖。
Intelligent part-to-picker systems are emerging in diverse industries as favorable for agile order fulfillment, wherein mobile racks are carried by robots and moved to stations where human pickers can pick items from them. Such systems raise the challenge of designing good work schedules for human pickers; they also give rise to a new class of operational scheduling problems in human-robot coordinated order picking systems. This work studies the problem of finding a suitable robot schedule that takes into account the variability of the working states of human pickers. A proposed model enables mobile racks with various workloads to be assigned to pickers, and the racks that are assigned to every picker to be scheduled to minimize the expected total picking time. The model takes into account schedule-induced changes in the working states of the pickers that may affect picking times. Pickers’ working state transitions are modeled based on data concerning real-world order picking in a warehouse. The problem is formulated as a stochastic dynamic program problem. An approximate dynamic programming (ADP)-based branch-and-price solution approach to solving this problem is proposed. The developed model is calibrated using data that were collected from a dominant e-commerce company in China. Counter-factual studies demonstrate that the proposed approach can solve a moderately sized problem with 50 racks in under two minutes. More importantly, the approach yields high-quality solutions with picking times that are 10% shorter than the actual solutions that did not consider schedule-induced fluctuations of pickers’ working states.