计算机工程与应用2025，Vol.61Issue(11)：342-350,9.DOI:10.3778/j.issn.1002-8331.2312-0315
基于图神经网络的柔性作业车间两阶段调度研究
Research on Two-Stage Joint Scheduling of Flexible Job Shop Based on Graph Neural Network
摘要
Abstract
Aiming at the flexible job shop scheduling problem with the goal of minimizing completion time and total energy consumption,an integrated algorithm framework based on graph neural networks and deep reinforcement learning is pro-posed.Firstly,the characteristics of the flexible job shop scheduling problem are analyzed,and a disjunctive graph is intro-duced to transform the problem into a sequential decision problem,which is then modeled as a Markov decision process.Secondly,a two-stage scheduling strategy is designed based on attention mechanism,which removes redundant scheduling states during the training process and significantly improves computational efficiency.Finally,a 2S-PPO algorithm based on proximal policy optimization is designed for training the two-stage scheduling strategy,aiming to achieve a joint sched-uling strategy that can quickly respond to process selection and machine allocation.Experimental proof through standard FJSP examples and FJSP examples with energy consumption instances demonstrates that the proposed algorithm has bet-ter learning performance and generalization performance compared to traditional priority scheduling rules and other deep reinforcement learning algorithms.关键词
柔性作业车间调度问题(FJSP)/图神经网络/深度强化学习/注意力机制Key words
flexible job-shop scheduling problem(FJSP)/graph neural network/deep reinforcement learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
魏琦,李艳武,谢辉,牛晓伟..基于图神经网络的柔性作业车间两阶段调度研究[J].计算机工程与应用,2025,61(11):342-350,9.基金项目
重庆市教委科学技术项目(KJQN202301216). （KJQN202301216）
