Multi-objective scheduling for complex assembly shops considering multiple human factors

The advancement of Industry 5.0 has driven a growing body of research that examines the impact of human factors on production processes. However, studies that simultaneously consider multiple types of human factors remain scarce. In this study, a comprehensive set of human factors, including workers’ skill proficiency, fatigue levels, interpersonal dynamics, and work experience, is incorporated into the assembly scheduling framework. Based on these considerations, the multi-objective scheduling problem in complex product assembly shops with parallel teams is investigated, with optimization objectives including makespan, transportation time, total waiting time, and team workload imbalance. To address this problem, an improved non-dominated sorting genetic algorithm is proposed. The algorithm features enhancement strategies, such as a destruction-reconstruction approach for optimizing the initial population and an improved evolutionary process. The proposed algorithm is evaluated against alternative algorithms using four case studies derived from actual production scenarios. The results demonstrate that the proposed method achieves superior solution quality and efficiency.