Research on Multi-objective Scheduling Optimization of Flexible Workshop Considering the Transportation Energy Consumption of AGV

Aiming at the problems of high energy consumption and low energy efficiency existing in the manufacturing industry, the multi-objective optimization problem of flexible job-shop scheduling considering the transportation energy consumption of AGVs was studied. Based on the traditional flexible job-shop scheduling problem, the transportation energy consumption problem of automatic guided vehicles was introduced, and a mathematical model aiming at the maximum completion time and the minimum total energy consumption was established. To solve this problem, the improved NSGA-II algorithm was adopted and a two-layer hybrid coding mechanism was designed. Through non-dominated sorting and crowded distance calculation, the diversity and convergence of Pareto solutions are guaranteed. The experiment was based on the actual data of a certain automotive parts workshop to verify the validity of the model and algorithm. The results show that the NSGA-II algorithm can effectively solve the flexible job-shop scheduling problem and also provide a scientific decision-making basis for manufacturing enterprises to implement differentiated scheduling strategies according to different demands.