Robust human-robot collaborative assembly line balancing problem under task time uncertainty

The human–robot collaborative assembly line balancing problem (HRC-ALBP), which incorporates collaborative robots (cobots), has recently garnered considerable attention for its potential to enhance productivity and support efficient, human-centered working environments. However, the natural variability in human task times introduces uncertainty, creating challenges for task assignment in collaborative settings. To address this issue, this study proposes a robust local search genetic–particle swarm optimisation (RGP) algorithm for solving the robust HRC-ALBP under task time uncertainty. The proposed algorithm generates line configurations while mitigating excessive solution conservativeness. Numerical experiments were conducted under task time variability to evaluate the performance of the proposed algorithm. The results verify that the algorithm produces more stable line configurations. Furthermore, sensitivity analyses were performed by varying both the number of cobots and the coefficient of variation of task times. Consequently, assembly lines designed using the RGP algorithm exhibit substantially higher reliability in sustaining uninterrupted production flow. Moreover, these analyses examine the operator assignment strategies required to establish a stable assembly line, depending on the number of cobots and the coefficient of variation of task times. The results indicate that stable assembly line design critically depends on appropriate operator assignment strategies.