Collaborative robot integration in mixed-model assembly line balancing with ergonomic risk considerations

Human–robot collaboration (HRC) offers new avenues for balancing efficiency and well-being in modern assembly systems. This study develops a Mixed-Integer Linear Programming (MILP) formulation for the Mixed-Model Assembly Line Balancing Problem (MMALBP) that explicitly embeds multi-dimensional ergonomic considerations into the balancing process. The proposed model, termed the Ergonomics-Oriented Mixed-Model Assembly Line Balancing Problem with Human–Robot Collaboration (Ergo-MMALBP-HRC), is formulated as a multi-objective optimization framework. It simultaneously minimizes cycle time, overall ergonomic risk (ER), and ergonomic category (EC) scores by integrating the physical, environmental, and psychosocial dimensions of ergonomics. Ergonomic risks are assessed using a multi-method framework and synthesized using a fuzzy logic–based approach to provide a holistic representation of task-level exposures. A computational analysis based on benchmark-derived configurations demonstrated the model’s effectiveness, showing that the inclusion of ergonomic constraints considerably mitigates risks without compromising productivity. On average, EC and ER decreased by 27% and 25%, respectively, across all test instances. The model’s practical applicability was further validated through two case studies. In the first, conducted on a power tool assembly line, the integration of five collaborative robots reduced cycle time by 23% and decreased ER and EC by 48% and 49.5%, respectively. The second case, adapted from a benchmark case study, confirmed similar improvements, three robots reduced cycle time by 28.4% and ER and EC by 64% and 59%. Overall, the findings demonstrate that embedding multi-dimensional ergonomic evaluations into HRC-based assembly line balancing promotes sustainable, human-centered manufacturing by enhancing both productivity and worker well-being.