数据采集与处理2024，Vol.39Issue(6)：1517-1531,15.DOI:10.16337/j.1004-9037.2024.06.019
基于深度强化学习的不确定作业车间调度方法
Deep Reinforcement Learning Model for Job Shop Scheduling Problems with Uncertainty
摘要
Abstract
Job shop scheduling problem(JSSP)is a non-deterministic polynomial(NP)-hard classical combinatorial optimization problem.In JSSP,it is usually assumed that the scheduling environment information is known and remains unchanged during the scheduling process.However,the actual scheduling process is often affected by many uncertain factors(such as machine failures and process changes).A proximal policy optimization with hybrid prioritized experience replay(HPER-PPO)scheduling algorithm is proposed for solving JSSPs with uncertainties.The JSSP is modeled as a Markov decision process where the state features,reward function,action space,and scheduling policy networks are designed.In order to improve the convergence of the proposed deep reinforcement learning model,a new hybrid prioritized experiential replay training method is proposed.The proposed scheduling method is evaluated on standard datasets and datasets generated based on standard datasets.The results show that in static scheduling experiments,the proposed scheduling model achieves more accurate results than existing deep reinforcement learning methods and priority dispatching rules.In dynamic scheduling experiments,the proposed scheduling model can achieve more accurate scheduling results in a reasonable time for JSSP with process order uncertainty.关键词
作业车间调度/深度强化学习/近端策略优化/优先经验重放Key words
job shop scheduling problem/deep reinforcement learning/proximal policy optimization/prioritized experience replay分类
信息技术与安全科学引用本文复制引用
吴新泉,燕雪峰,魏明强,关东海..基于深度强化学习的不确定作业车间调度方法[J].数据采集与处理,2024,39(6):1517-1531,15.基金项目
"十四五"装备预研项目(JZX7Y20210401001801). （JZX7Y20210401001801）
