Model and metaheuristic for human–robot collaboration assembly line worker assignment and balancing problem

Given the Industry 5.0 trend towards human-centric and resilient industries, workers continue to be considered as a valuable and irreplaceable resource. Human–robot collaboration (HRC) is a promising production mode that combines the advantages of both human workers and robots, resulting in improved productivity and reduced ergonomic risks for workers. In this study, we present one of the first attempts to address the human–robot collaboration assembly line worker assignment and balancing problem (HRCALWABP), which allows workers and robots to perform tasks in parallel or in collaboration. Workers are heterogeneous, leading to variations in task execution times. We formulate a mixed-integer linear programming (MILP) model that minimizes the cycle time and propose a tight lower/upper bound. A rebalancing process is further considered in this study. Due to the complexity of HRCALWABP, we develop an improved adaptive neighborhood simulated annealing algorithm (IASA). An adaptive mechanism is applied to the IASA to dynamically adjust the selection probability of the designed neighborhood structures and operators, and a restart mechanism is developed for improving the search capacity. Extensive computational experiments are conducted to evaluate the performance of both the MILP and IASA, and the results confirm the superiority of the IASA compared to the MILP and other existing algorithms.