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

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. • Presented task allocation and scheduling method with efficiency and fatigue. • Proposed a cost-accurate method aligned better with practical scenarios. • Presented scalable method for multiple humans and robots. • Validated effectiveness through application, extension, and scalability studies.