Multiobjective optimization allocation of multi-skilled workers considering the skill heterogeneity and time-varying effects in unit brake production lines

Abstract

The optimal allocation of multi-skilled workers in labor-intensive industries can improve production capacity and reduce production costs. In actual production, the efficiency of workers will change with time due to their proficiency, fatigue, and other effects. In this article, we attempt to solve the problem of multi-skilled workers allocation in unit brake production lines considering the heterogeneity of skills and time-varying effects. A nonlinear mixed-integer programming model is established, which fully considers the impact of worker efficiency due to proficiency, fatigue, and multi-task rest recovery. The product production cycle and worker cost are the two objectives of the optimization solution. An enhanced NSGA-II algorithm that combines the improved NSGA-II algorithm and the variable neighborhood search (VNS) algorithm is used to solve the multiobjective optimization problem. Finally, the weighted ideal point method is used to obtain the Pareto optimal solution. The application case of a unit brake production is considered to evaluate the proposed model. The results indicate that the time cost and salary cost of workers are reduced by 8.03% and 18.91% compared with the original scheduling. The scheduling model considering learning, fatigue and recovery factors is more suitable for the actual production situation, ensuring the completion time and reducing the labor cost.