A genetic algorithm for the multi-objective optimization of mixed-model assembly line based on the mental workload

The increasing complexity of product varieties and productions leads to higher mental workload in the mixed-model assembly line (MMAL). Mental workload can improve product quality and guarantee the efficiency simultaneously. However, little research has been done on balancing the production quality and efficiency based on the effect of mental workload and complexity in the MMAL. This study aims to propose a mathematical model to formulate the multi-objective MMAL problem and the genetic algorithm is applied for problem solving due to the computational complexities. A numerical example is used to demonstrate the effectiveness of the proposed approach. The results show that incorporating the impact of mental workload on performance into account can make the rolled throughput yield (RTY) and efficiency balance when designing the MMAL. Moreover, we also verify that improving the experience of the operators can mitigate the impact of mental workload on the quality and efficiency.