Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling

• Published in: CIRP journal of manufacturing science and technology 2023-12, Vol.47, p.71-85
• Main Authors: Nourmohammadi, Amir, Ng, Amos H.C., Fathi, Masood, Vollebregt, Janneke, Hanson, Lars
• Format: Article
• Language: English
• Subjects: Användarcentrerad produktdesign; Digital human modeling; lNSGA-II; Mathematical mode; Mathematical model; Mixed-model assembly line balancing; Multi-objective optimization; Musculoskeletal risks; NSGA-II; User Centred Product Design; VF-KDO; Virtual Production Development (VPD)
• ISSN: 1755-5817; 1878-0016
• DOI: 10.1016/j.cirpj.2023.09.002

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.