App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Graph reinforcement learning for dynamic scheduling of two-stage distributed hybrid flow shop considering assembly coordination

Pei Zhijie1,2
Yang Xiaoying3
Li Xinyan2
Yang Xin1
1. Business School, Henan University of Science and Technology, Luoyang 471023, China
2. School of Economics and Management, Huanghe Jiaotong University, Jiaozuo 454950, China
3. School of Mechanical Engineering, Henan University of Science and Technology, Luoyang 471003, China
DOI: 10.19734/j.issn.1001-3695.2026.01.0004

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, a dynamic scheduling method based on graph reinforcement learning (GRL) with assembly coordination is developed. The method constructs a graph-structured representation integrating operation coupling and shop floor states. It employs a Convolutional Neural Network (CNN) and a dual Graph Attention Network (GAT) to capture intra-factory local topological features and cross-stage global coordination features. A Proximal Policy Optimization (PPO) algorithm is further applied 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 (IWOA) , 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 reduces 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.

Foundation Support

工业和信息化部项目(2206-410305-04-02-409831)
河南省重点研发专项(231111222600)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.01.0004
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-12] Accepted Paper

Cite This Article

裴志杰, 杨晓英, 李新艳, 等. 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). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0004. (Pei Zhijie, Yang Xiaoying, Li Xinyan, et al. Graph reinforcement learning for dynamic scheduling of two-stage distributed hybrid flow shop considering assembly coordination [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.01.0004. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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