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Task allocation and scheduling to enhance human-robot collaboration in production line by synergizing efficiency and fatigue

文献类型： 外文期刊

作者： Zeng, Fan 1 ; Fan, Changxiang 2 ; Shirafuji, Shouhei 3 ; Wang, Yusheng 1 ; Nishio, Masahiro 4 ; Ota, Jun 1 ;

作者机构： 1.Univ Tokyo, Ctr Engn RACE, Sch Engn, Res Artifacts, Tokyo 1138656, Japan

2.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Guangdong, Peoples R China

3.Kansai Univ, Fac Engn Sci, Dept Mech Engn, Osaka 5650842, Japan

4.Toyota Motor Co Ltd, Adv R&D & Engn Co, R&D & Engn Management Div, Strateg R&D Planning Dept, Toyota 4710826, Japan

关键词： Human-robot collaboration; Task allocation and scheduling; Human fatigue; Production line

期刊名称：JOURNAL OF MANUFACTURING SYSTEMS （ 影响因子：14.2； 五年影响因子：13.9 ）

ISSN： 0278-6125

年卷期： 2025 年 80 卷

页码：

收录情况： SCI

摘要： Introducing robots to assist humans in production lines can reduce human fatigue, but efficiency should also not be overlooked. Therefore, task allocation and scheduling, which determine who performs tasks and when they start and finish, should consider both efficiency and fatigue in human-robot collaboration. Efficiency needs to be maximized while fatigue needs to be minimized, necessitating a compromise solution to balance these conflicting objectives. Task allocation guided by multiple objectives is computationally more complex. Furthermore, the production line, with its numerous components and tasks, typically has a larger search space, especially in scenarios involving multiple humans and robots. This complexity makes it challenging for most current human-robot task allocation methods to effectively address such problems. Thus, a new task allocation and scheduling method to balance efficiency and fatigue is proposed in this paper. It reallocates initial sequential human actions to all the humans and robots, obtains locally optimal solutions by multi- heuristics search with efficiency and fatigue synergized, and a fast-converging greedy search is then employed to refine these locally optimal solutions to approach the global optimum. What is more, the proposed method was applied to a laboratory-constructed production line and extended to more complex scenarios involving four different setups, as well as the scalability experiment, demonstrating superior task allocation and scheduling capabilities in balancing the efficiency and fatigue of complex scenarios.

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