聊城大学学报（自然科学版）2026，Vol.39Issue(2)：192-204,273,14.DOI:10.19728/j.issn1672-6634.2025070005
基于端到端深度强化学习的多订单动态柔性作业车间调度方法
An end-to-end deep reinforcement learning method for multi-order dynamic flexible job shop scheduling problem
摘要
Abstract
To address the Dynamic Flexible Job Shop Scheduling Problem with Order Random Arrival(DFJSP_ORA),a modeling and solution framework tailored for the actual production environment is pro-posed.First,a mathematical model is formulated to minimize the maximum completion time.The fluid model is then introduced to continuously approximate system behavior and extract key state features.Sub-sequently,the scheduling process is modeled as a Markov Decision Process(MDP),and a deep reinforce-ment learning method based on Proximal Policy Optimization(PPO)is developed to solve the problem.It combines the discrete action space driven by composite rules and the strategy optimization mechanism driv-en by the advantage function,achieving efficient decision-making in dynamic environments.Experimental results demonstrate that the proposed approach performs well in dynamic scheduling scenarios and effec-tively handles uncertainty and complexity in production,providing an efficient and flexible solution for DFJSP_ORA.关键词
柔性作业车间调度/深度强化学习/近端策略优化/流体模型/最大完工时间Key words
flexible job shop scheduling/deep reinforcement learning/proximal policy optimization/fluid model/maximum completion time分类
机械制造引用本文复制引用
王旭,李寰,韩玉艳,王玉亭,王雅坤..基于端到端深度强化学习的多订单动态柔性作业车间调度方法[J].聊城大学学报（自然科学版）,2026,39(2):192-204,273,14.基金项目
国家自然科学基金项目(61973203,61803192,62106073,61966012) （61973203,61803192,62106073,61966012）
山东省自然科学基金项目(ZR2023MF022) （ZR2023MF022）
聊城大学光岳青年创新团队(LCUGYTD2022-03) （LCUGYTD2022-03）
原生数据库架构创新及高性能核心技术(ZTZB-23-990-024)资助 （ZTZB-23-990-024）
