一种基于强化学习的作业车间动态调度方法

作     者：魏英姿 赵明扬 WEI Ying-Zi;ZHAO Ming-Yang

作者机构：Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016Shenyang Ligong University Shenyang 110168 Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 

出 版 物：《自动化学报》 (Acta Automatica Sinica)

年 卷 期：2005年第31卷第5期

页      面：765-771页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：国家重点基础研究发展计划(973计划) 

主　　题：强化学习 作业车间 动态调度 复合标准 

摘      要：Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in a dynamic job-shop. Existing scheduling approaches seldom address machine selection in the scheduling process. Composite rules, considering both machine selection andjob selection, are proposed in this paper. The dynamic system is trained to enhance its learning and adaptive capability by a reinforcement learning (RL) algorithm. We define the conception of pressure to describe the system feature. Designing a reward function should be guided by the scheduling goal to accurately record the learning progress. Competitive results with the RL-based approach show that it can be used as real-time scheduling technology.