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.

与工业 5.0 相一致，人体工学因素最近在平衡装配线以增强以人为本的方面受到更多关注。与此同时，当今的大规模定制趋势使制造商能够抵消不同产品变体的装配线。因此，本研究通过考虑工人姿势来解决混合模型装配线平衡问题（MMALBP）。数字人体建模和姿势评估技术用于使用快速全身分析 (REBA) 的方法来评估与工作相关的肌肉骨骼疾病的风险。由此产生的 MMALBP 被制定为混合整数线性规划 (MILP) 模型，同时考虑三个目标：循环时间、工作站的最大人体工程学风险和总体人体工程学风险。增强型非支配排序遗传算法（E-NSGA-II）是通过结合生成邻域解决方案的局部搜索程序和确保选择有前途的解决方案的多标准决策机制而开发的。E-NSGA-II 以 Epsilon 约束、MOGA 和 NSGA-II 为基准，同时解决案例研究和来自文献的测试问题。计算结果表明，E-NSGA-II 可以找到有前途的帕累托前沿解决方案，同时在性能指标方面主导所考虑的方法。E-NSGA-II 结果的稳健性通过单向方差分析统计检验进行评估。结果分析表明，当同时考虑所有三个目标时，可以实现工作站之间时间和人体工程学负载的平滑分配。