引用本文:谭伟华,吴亮红,李哲,袁小芳.改进教学优化算法求解执行器配置与生产调度协同优化问题[J].控制理论与应用,2026,43(4):865~873.[点击复制]
TAN Wei-hua,WU Liang-hong,LI Zhe,YUAN Xiao-fang.Improved teaching-learning-based optimization algorithm for collaborative optimization of end-effector allocation and production scheduling[J].Control Theory & Applications,2026,43(4):865~873.[点击复制]
改进教学优化算法求解执行器配置与生产调度协同优化问题
Improved teaching-learning-based optimization algorithm for collaborative optimization of end-effector allocation and production scheduling
摘要点击 161  全文点击 23  投稿时间：2024-04-18  修订日期：2025-12-17
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DOI编号  10.7641/CTA.2024.40224
  2026,43(4):865-873
中文关键词  柔性作业车间调度  资源配置  多目标优化  教学优化算法
英文关键词  flexible job-shop scheduling  resource allocation  multi-objective optimization  teaching-learning-based optimization algorithm
基金项目  国家重点研发计划项目(2021YFB3301800), 国家自然科学基金项目(62373146), 湖南省自然科学基金项目(2022JJ30265), 湖南省科技人才托举工 程项目(2022TJ–Q03)资助.
作者单位E-mail
谭伟华 湖南科技大学信息与电气工程学院 tanwh@hnust.edu.cn 
吴亮红 湖南科技大学信息与电气工程学院  
李哲 湖南大学电气与信息工程学院  
袁小芳* 湖南大学电气与信息工程学院 yuanxiaofang@hnu.edu.cn 
中文摘要
      在实际柔性作业车间中, 执行器配置与生产调度的高效协同有利于提高生产决策的全局性, 从而提升车间 的柔性加工能力. 针对执行器配置与生产调度的多目标协同优化问题, 以最小化综合生产成本和完工时间为优化 目标, 构建了混合整数规划模型, 使得小规模问题通过可以Gurobi精确求解. 本文提出了一种两阶段离散教学优化 算法, 设计了学习强度自适应调整方法和改进关键工序移动策略, 以提升算法的效率和多目标平衡搜索能力. 通过 仿真实验, 分析验证了所提协同优化方法的优越性和所提算法的有效性.
英文摘要
      In the practical flexible job-shop, the efficient collaboration between end-effector allocation and production scheduling benefits providing global decision-making, thus facilitating the flexibility of the workshop. To address the collaborative optimization problem of end-effector allocation and production scheduling, a mixed-integer programming model is constructed to minimize the comprehensive production cost and makespan, enabling the accurate solution of smallscale problems via Gurobi. In addition, a dual-stage discrete teaching-learning-based optimization algorithm is proposed, incorporating a distance-based adaptive adjustment strategy for learning coefficient and an improved neighborhood search based on critical operation movement to facilitate efficiency and objective balancing. Through simulation experiments, the superiority of the proposed collaborative optimization method and the effectiveness of the proposed algorithm are analyzed and validated.