Integrating collaboration scenarios and workforce individualization in collaborative assembly line balancing

Abstract

Integrating collaborative robots into assembly lines represents a significant opportunity for industries to execute tasks autonomously or support human workers in response to the advancements of Industry 4.0. Human-robot collaboration (HRC) is an appropriate solution to enhance the productivity of manual systems compared to traditional robots. However, to ensure the success of HRC implementation, it is necessary to investigate the production systems, considering several influencing factors. Workforce diversity can be mentioned as one of the factors since workers may possess different skills and experience levels, as well as varying levels of fatigue. Therefore, creating a realistic and effective optimization model that includes workforce diversity is crucial. This study proposes a mathematical model to optimize a human-robot collaborative assembly line performance to minimize the cycle time. The model integrates several collaborative scenarios (i.e. sequential, simultaneous, supportive and all possible combinations), and the workforce differences are considered in terms of skill level and fatigue, allowing the flexible selection of collaboration scenarios across the assembly line and assigning workers and cobots to stations based on individual characteristics. Finally, the proposed model is applied in a case study to provide results and some managerial insights for practitioners.