Reinforcement Learning Based Job Shop Scheduling with Machine Choice

被引：0
|
作者：
Wang, Chao [1 ]
Zhang, Hongbin [1 ]
Guo, Jing [1 ]
Chen, Ling [2 ]
机构：
[1] YangZhou Polytech Inst, Dept Elect & Informat Engn, Beijing, Peoples R China
[2] Yangzhou Univ, Coll Informat Engn, Yangzhou 225009, Jiangsu, Peoples R China
来源：
ADVANCED MANUFACTURING TECHNOLOGY, PTS 1-3 | 2011年 / 314-316卷
基金：
中国国家自然科学基金;
关键词：
Dynamic scheduling; Reinforcement Learning; Machine choice; Cohesion;
D O I：
10.4028/www.scientific.net/AMR.314-316.2172
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Job shop scheduling is a key technology in modern manufacturing. Scheduling performance will decide the enterprises' core competitiveness. In this paper, improved reinforcement learning with cohesion is used in dynamic job shop environment, and it eased the contradiction of precocious and slow convergence. Also the machine choice is considered. So the dual scheduling which included job and machine is achieved in this system. And it obtains better results through the experiments. The utilization of equipments and the emergency handling capacity can be improved in the dynamic environment.
引用
收藏
页码：2172 / +
页数：2
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