计算机科学
返工
动态优先级调度
强化学习
作业车间调度
公平份额计划
单调速率调度
两级调度
调度(生产过程)
工作车间
抽奖日程安排
工业工程
分布式计算
流水车间调度
数学优化
地铁列车时刻表
运筹学
人工智能
嵌入式系统
操作系统
布线(电子设计自动化)
工程类
数学
作者
Libing Wang,Xin Hu,Yin Wang,Sujie Xu,Shijun Ma,Kexin Yang,Zhijun Liu,Weidong Wang
标识
DOI:10.1016/j.comnet.2021.107969
摘要
Job-shop scheduling problem (JSP) is used to determine the processing order of the jobs and is a typical scheduling problem in smart manufacturing. Considering the dynamics and the uncertainties such as machine breakdown and job rework of the job-shop environment, it is essential to flexibly adjust the scheduling strategy according to the current state. Traditional methods can only obtain the optimal solution at the current time and need to rework if the state changes, which leads to high time complexity. To address the issue, this paper proposes a dynamic scheduling method based on deep reinforcement learning (DRL). In the proposed method, we adopt the proximal policy optimization (PPO) to find the optimal policy of the scheduling to deal with the dimension disaster of the state and action space caused by the increase of the problem scale. Compared with the traditional scheduling methods, the experimental results show that the proposed method can not only obtain comparative results but also can realize adaptive and real-time production scheduling.
科研通智能强力驱动
Strongly Powered by AbleSci AI