引用本文:邱佳龙,龚文引,张国辉,卢超.个体简化的遗传规划求解动态多柔性作业车间调度问题[J].控制理论与应用,2025,42(12):2587~2597.[点击复制]
QIU Jia-long,GONG Wen-yin,ZHANG Guo-hui,LU Chao.Genetic programming with individual simplification policy for dynamic multi-flexible job shop scheduling problem[J].Control Theory & Applications,2025,42(12):2587~2597.[点击复制]
个体简化的遗传规划求解动态多柔性作业车间调度问题
Genetic programming with individual simplification policy for dynamic multi-flexible job shop scheduling problem
摘要点击 155  全文点击 21  投稿时间：2024-12-31  修订日期：2025-11-08
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DOI编号  10.7641/CTA.2025.40653
  2025,42(12):2587-2597
中文关键词  动态柔性作业车间调度  工序顺序柔性  遗传规划
英文关键词  dynamic flexible job shop scheduling problem  flexible process sequence  genetic programming
基金项目  国家自然科学基金项目(52175490,51805495)资助.
作者单位E-mail
邱佳龙 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  
张国辉 郑州航空工业管理学院  
卢超* 中国地质大学(武汉)计算机学院 luchao@cug.edu.cn 
中文摘要
      研究动态柔性作业车间调度问题(DFJSP)对有效控制生产过程,提升企业经济盈利能力具有重要意义.现 有研究大多仅考虑机器选择柔性及动态生产场景,但实际生产中还存在更复杂的工序顺序柔性问题.在定制化装备 制造等场景下,作业加工工序可按工艺要求划分为若干组,同组内工序无强制加工顺序约束,不同组间则需遵循严 格加工顺序.这种工序顺序柔性的引入显著增加问题搜索空间复杂度,导致求解时间大幅延长.因此,本文结合定 制化装备制造车间的实际生产需求,综合考虑机器选择柔性,工序顺序柔性及新作业到达的动态特性,提出动态多 柔性作业车间调度问题(DMFJSP),其优化目标为最小化平均流程时间.为求解该问题,本文提出一种基于个体简化 的遗传规划(GP)方法,通过引入动态终端节点频率分析个体结构复杂度,并在适应度评估阶段设置惩罚函数引导种 群进化,进而简化个体结构,缩短GP训练时间.为验证所提方法的有效性,本文在3种不同生产场景下开展测试,结 果表明,该方法在保证求解质量的同时,可显著缩短计算时间.面对更大规模和复杂问题时,其收敛速度快,能在合 理时间内找到满意解.
英文摘要
      Studying the dynamic flexible job-shop scheduling problem (DFJSP) is of great significance for effectively controlling production processes and enhancing enterprises’ economic profitability. Existing studies mostly only consider machine selection flexibility and dynamic production scenarios; however, a more complex issue of flexible process sequence exists in practical production. Specifically, in scenarios like customized equipment manufacturing, job processing opera tions can be divided into several groups according to process requirements. There are no mandatory processing sequence constraints apply to operations within the same group, whereas strict processing sequence constraints must be followed between different groups. The introduction of this flexible process sequence significantly increases the complexity of the problem’s search space, leading to a substantial extension of solution time. Therefore, by integrating the practical produc tion needs of customized equipment manufacturing workshops, this paper comprehensively considers machine selection f lexibility, flexible process sequence, and the dynamic characteristic of new job arrivals, and proposes the dynamic multi f lexible job-shop scheduling problem (DMFJSP) with the optimization objective of minimizing the average flow time. To solve this problem, a genetic programming (GP) method based on individual simplification is proposed which analyzes the structural complexity of individuals by introducing dynamic terminal node frequency, and sets a penalty function during the fitness evaluation stage to guide population evolution―ultimately simplifying individual structures and reducing GP training time. To verify the effectiveness of the proposed method, tests are conducted under three different production sce narios. The results demonstrate that the method can significantly shorten computation time while ensuring solution quality; when addressing larger-scale and more complex problems, it exhibits fast convergence and can obtain satisfactory solutions within a reasonable time frame.