Balancing and scheduling assembly lines with human-robot collaboration tasks

In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity workers’ well-being, provided that optimal allocation tasks available resources determined. This study investigates line balancing problem (ALBP), considering HRC. problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans collaborative robots share same workplace simultaneously perform parallel or collaboration. Driven by need solve more complex line-balancing problems found automotive industry, this aims address ALBP-HRC with cycle time number operators (humans robots) as primary secondary objective, respectively. addition traditional ALBP constraints, human robot characteristics, terms task times, allowing multiple at stations, their joint/collaborative are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed customized solution representation neighborhood search designed fit characteristics. Furthermore, SA features an adaptive selection mechanism enables utilize its exploration history dynamically choose appropriate evolves. The MILP implemented on real cases taken from industry stations for computational results over different show produces promising solutions compared other swarm intelligence algorithms, namely genetic algorithm, particle optimization, artificial bee colony. comparisons human/robot versus HRC settings case indicate significant improvement when permissible stations.