引用本文:袁志华,卢超,余飞,龚文引.考虑双层学习效应的双资源柔性作业车间调度[J].控制理论与应用,2025,42(11):2296~2309.[点击复制]
YUAN Zhi-hua,LU Chao,YU Fei,GONG Wen-yin.Dual-resource flexible job shop scheduling considering dual-layer learning effects[J].Control Theory & Applications,2025,42(11):2296~2309.[点击复制]
考虑双层学习效应的双资源柔性作业车间调度
Dual-resource flexible job shop scheduling considering dual-layer learning effects
摘要点击 3638  全文点击 128  投稿时间：2025-01-01  修订日期：2025-10-08
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DOI编号  10.7641/CTA.2025.50001
  2025,42(11):2296-2309
中文关键词  柔性作业车间调度问题  工人的双层学习效应  Memetic算法
英文关键词  flexible job shop scheduling problem  dual-layer learning effects of workers  Memetic algorithm
基金项目  国家自然科学基金项目(52175490,51805495),国家重点计划研发项目(2021YFB3301600)资助.
作者单位E-mail
袁志华 中国地质大学(武汉)计算机学院 yuanzhihua@cug.edu.cn 
卢超* 中国地质大学(武汉)计算机学院 luchao@cug.edu.cn 
余飞 中国地质大学(武汉)计算机学院  
龚文引 中国地质大学(武汉)计算机学院  
中文摘要
      在工业5.0时代,“以人为本”的智能制造成为研究热点.针对双资源柔性作业车间调度问题,已有研究考虑 了工人的学习效应,但尚未涉及工人对工件和机器双层学习效应对生产效率的影响.为此,本文首次提出了考虑工 人双层学习效应的双资源柔性作业车间调度问题,并构建以最大完工时间为优化目标的数学模型.为求解该问题, 提出了一种改进的Memetic算法,主要改进包括:设计符合问题特性的3层编码,提出主动调度解码策略提升解的质 量, 开发种群初始化策略增强多样性,并设计融合交叉变异更新与变邻域搜索策略以提高全局探索与局部寻优能 力. 最后,通过对比实验验证了算法和模型的有效性.
英文摘要
      In the era of Industry 5.0, human-centred intelligent manufacturing has become a hot research topic. For the dual-resource flexible job shop scheduling problem, studies have considered the learning effect of workers, but have not yet addressed the impact of the dual-layer learning effect of workers on jobs and machines on production efficiency. For this reason, this paper proposes for the first time a two-resource flexible job shop scheduling problem considering the dual layer learning effect of workers, and constructs a mathematical model with maximum completion time as the optimisation objective. To solve the problem, an improved Memetic algorithm is proposed, and the main improvements include: De signing a three-layer encoding that meets the problem characteristics, proposing an active scheduling decoding strategy to improve the solution quality, developing a population initialisation strategy to enhance the diversity, and designing a fusion of cross-variance updating and variable-neighbourhood searching strategies to improve the ability of global exploration and local optimisation. Finally, the effectiveness of the algorithm and model is verified by comparison experiments.