控制理论与应用2026，Vol.43Issue(8)：1717-1725,9.DOI:10.7641/CTA.2025.40638
知识驱动的3D打印生产能效调度问题研究
Research on knowledge-driven 3D printing production energy efficiency scheduling problem
摘要
Abstract
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.关键词
3D打印调度/多目标优化/知识驱动/强化学习Key words
3D printing scheduling/multi-objective optimization/knowledge-driven/reinforcement learning引用本文复制引用
韩凯歌,吴斌,陈仁胜,刘必强,童华刚..知识驱动的3D打印生产能效调度问题研究[J].控制理论与应用,2026,43(8):1717-1725,9.基金项目
国家重点研发计划项目(2022YFB3805201)资助.Supported by the National Key Research and Development Program(2022YFB3805201). （2022YFB3805201）
