Optimization of Mixed-Model Multi-Manned Assembly Lines for Fuel–Electric Vehicle Co-Production Under Workstation Sharing

Abstract

With the rapid transformation of the automotive industry towards electric vehicles, how to achieve efficient mixed-line production of electric vehicles and fuel vehicles has become a key challenge for modern assembly systems. This study investigated the balancing problem of a mixed-model multi-manned assembly line, considering workstation sharing (MMuALBP-WS), and developed a deterministic multi-objective model that integrates the heterogeneity of tasks and the coordination of shared workstations. An improved genetic algorithm was proposed, whose decoding mechanism enables different types of electric vehicle and fuel vehicle tasks to achieve dynamic collaboration within the shared workstations. A real case study from the chassis assembly line of Company W demonstrated the effectiveness of the proposed method, achieving a 25% reduction in the number of workstations, a 27% decrease in the total number of workers, and a 23.56% increase in average workstation utilization. The results confirmed that the workstation sharing mechanism significantly improved production balance, labor utilization, and flexibility, providing a practical and scalable optimization framework for the mixed-model assembly system in the era of the transition from electric vehicles to fuel vehicles. In addition to its practical significance, this study enhances the understanding of mixed-model multi-manned line balancing by incorporating workstation-sharing logic into both the mathematical modeling and optimization process, offering a theoretical basis for future extensions to more complex production environments.