Balancing and scheduling of assembly line with multi-type collaborative robots

Abstract

Human–robot collaboration (HRC) is a promising production mode that is in line with the vision of human-centered Industry 4.0. HRC contributes to the improvement of productivity as well as the reduction of workers’ ergonomic risk. In this study, we present one of the first attempts to address the assembly line balancing problem with multi-type collaborative robots (cobots), which allows human and robots to perform tasks in parallel or in collaboration. A mixed-integer programming (MIP) model is formulated to minimize the cycle time and a tight lower bound is proposed. We further propose a multi-objective model and an extended model to expand the scope of the study. Due to the complexity, an adaptive neighborhood simulated annealing algorithm (ANSA) is developed with the designed neighborhood operators and structures. Furthermore, an adaptive mechanism is applied to the ANSA to dynamically update the weights of the neighborhood structures based on historical information. Extensive computational experiments and a real case study are conducted to verify the superiority of ANSA. We further compare the application of diverse collaboration modes, i.e., sequential, simultaneous and supportive modes. The results also indicate that a suitable type of robot can improve productivity and the utilization rate of robots.