计算机应用研究2026，Vol.43Issue(9)：2630-2638,9.DOI:10.19734/j.issn.1001-3695.2026.01.0004
考虑装配协同的分布式两阶段混合流水车间图强化学习动态调度
Graph reinforcement learning for dynamic scheduling of two-stage distributed hybrid flow shop considering assembly coordination
摘要
Abstract
To address the coordination mismatch between cross-factory production and assembly in the two-stage distributed hybrid flow shop scheduling problem(TSDH-FSSP)under dynamic environments,this paper developed a dynamic scheduling method based on graph reinforcement learning(GRL)with assembly coordination.The method constructed a graph-structured representation integrating operation coupling and shop floor states.It employed a convolutional neural network(CNN)and a dual graph attention network(GAT)to capture intra-factory local topological features and cross-stage global coordination fea-tures.It further applied a proximal policy optimization(PPO)algorithm to achieve end-to-end autonomous decision-making under a composite reward mechanism.Simulation results show that the proposed method outperforms the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ),the improved whale optimization algorithm(I WO A),and deep reinforcement learning scheduling methods(DRL-GCN and DRL-TF)in terms of makespan and relative percentage deviation(RPD)under dynamic job insertion scenarios.The fixed-rule strategy in the second stage reduced training time by 27.11％while maintaining high solution quality.A real-world bearing manufacturing case verifies the effectiveness and robustness of the proposed method in complex dynamic environments and demonstrates its potential for improving production-assembly coordination in distributed manufacturing systems.关键词
两阶段分布式混合流水车间/装配协同/图强化学习/动态调度/图注意力网络Key words
two-stage distributed hybrid flow shop/assembly coordination/graph reinforcement learning/dynamic schedu-ling/graph attention network分类
机械制造引用本文复制引用
裴志杰,杨晓英,李新艳,杨欣..考虑装配协同的分布式两阶段混合流水车间图强化学习动态调度[J].计算机应用研究,2026,43(9):2630-2638,9.基金项目
工业和信息化部项目(2206-410305-04-02-409831) （2206-410305-04-02-409831）
河南省重点研发专项(231111222600) （231111222600）
