Can we work more safely and healthily with robot partners? A human-friendly robot-human-coordinated order fulfillment scheme

• 成果类型: Article
• 署名作者: Sheu, Jiuh-Biing; Choi, Tsan-Ming
• 署名单位: National Taiwan University; University of Liverpool; University of Liverpool
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13899
• 发表日期: 2023
• 页码: 794-812
• 关键词: automated warehousing system; data-driven stochastic optimal control; intelligent logistics; mobile rack; robot-human coordination

摘要

This work addresses the issue of intelligent robot-human-coordinated parts-to-picker order fulfillment carried out in a human-friendly manner. One unique feature of the proposed approach involves integrating a real-time data-driven stochastic-dynamic model with a fatigue accumulation function. The optimal solutions help achieve coordination between human pickers and robots, such that robots can agilely adapt to the coordinated pickers' efficiency and fatigue conditions. Specifically, the proposed method estimates human pickers' instantaneous performance and robot queue lengths, which are then fed back in real time as indexes to adjust robots' speeds of handling racks and moving them to human pickers. Using data that are provided by a giant electronic commerce (e-commerce) company, our analyses demonstrate that the proposed robot-picker coordination system permits alleviating a picker's fatigue without much influence on picking efficiency. In particular, a picker's accumulated fatigue can be reduced by 53.74% at the expense of lowering picker efficiency by 14.79% if the proposed robot-picker coordination system is applied in the focal firm of the study case. Through our scenario design and sensitivity analysis, additional findings and managerial insights, including the rules of human-friendly robot behaviors for coordination with human pickers in different operational scenarios are provided. They facilitate the development of human-friendly intelligent robot-human-coordinated order-fulfillment systems for intelligent logistics operations.