知识驱动的3D打印生产能效调度问题研究OA

As a key technology of intelligent manufacturing,3D printing has been put into practical application in many enterprises.With the proposal of the"dual carbon"strategy,the energy consumption problem in 3D printing has gradually attracted attention.This paper takes minimizing the maximum completion time and energy consumption as the goal,and constructs a multi-objective 3D printing production scheduling model considering multiple printing directions.To solve this model,a knowledge-driven multi-population evolutionary algorithm is proposed.Based on the knowledge of the problem,the population partition strategy and the local search operator are designed to enhance the depth and breadth of the algorithm search,and the local search strategy is executed in combination with the reinforcement learning mechanism to improve the efficiency of the local search.Simulation experimental results show that the improved strategy proposed in this paper can effectively improve the algorithm performance,making it an effective method for solving multi-objective 3D printing scheduling.