This study presents a mixed-integer linear programming (MILP) model designed to minimize the makespan of simple assembly lines by integrating the effects of learning and fatigue among workers. A learning curve model with a fatigue-dependent learning rate is incorporated to capture variations in worker efficiency over time. The proposed model optimally assigns tasks and workers to stations, ensuring that individual learning abilities and fatigue accumulation are considered in the optimization process.

To validate the approach, the model is applied to a benchmark problem, where different worker assignment strategies are analyzed. The results indicate that random worker allocation and balanced workload distribution can lead to significantly longer makespans, emphasizing the critical role of aligning task assignments with workers' learning capacities and fatigue progression. By accounting for these dynamic factors, the proposed model enhances the realism and effectiveness of assembly line balancing. The findings underscore the potential of integrating learning and fatigue considerations to improve efficiency in assembly line operations.