Research on Production Line Balance Optimization Based on Improved PSO-GA Algorithm

According to the complementary characteristics of process planning and workshop scheduling, integrating them can improve the performance of intelligent manufacturing systems. However, traditional precise methods cannot effectively solve the problem of large-scale integrated process planning and scheduling (IPPS). In order to minimize the maximum completion time, this paper establishes a resource scheduling model for intelligent manufacturing workshops. On this basis, a combination of genetic algorithm and particle swarm optimization algorithm is proposed. In order to further improve the search and optimization ability, a hierarchical encoding and decoding method is designed. Finally, through the test of benchmark cases and actual production and processing problems, the superiority and effectiveness of the algorithm are verified.