Balancing human–robot collaborative assembly lines: A constraint programming approach

Abstract

The advent of Industry 5.0 and advancements in collaborative robot (cobot) technology have driven many industries to adopt human–robot collaboration (HRC) in their assembly lines. This collaborative approach, which combines human expertise with robotic precision, necessitates an optimized method for balancing and scheduling tasks and operators across stations. This study proposes various constraint programming (CP) models tailored to straight and U-shaped assembly layouts, with objectives such as minimizing the number of stations, reducing cycle time, and minimizing costs. To enhance real-world applicability, the models consider the presence of diverse humans and cobots with varying skills and energy requirements working collaboratively or concurrently on assembly tasks. Additionally, practical constraints are addressed, including robot tool changes, zoning, and technological needs. Computational results demonstrate the superior efficiency of the proposed CP models over state-of-the-art mixed-integer programming models, validated through a case study and a comprehensive set of test problems. The results indicate that U-shaped layouts offer greater flexibility than straight-line configurations, particularly in reducing cycle time. Furthermore, higher HRC levels, including more humans and cobots, can significantly improve the number of stations, cycle time, and cost by up to 50%, 29%, and 36%, respectively.