Dynamic job-shop scheduling using reinforcement learning agents

被引：185
作者：
Aydin, ME
Öztemel, E
机构：
[1] Sakarya Univ, Dept Ind Engn, TR-54040 Adapazari, Turkey
[2] Tubitak Marmara Res Ctr, BTAE, Artificial Intelligence Grp, Gebze, Kocaeli, Turkey
来源：
ROBOTICS AND AUTONOMOUS SYSTEMS | 2000年 / 33卷 / 2-3期
关键词：
intelligent agents; reinforcement learning; Q-III learning; dynamic job-shop scheduling;
D O I：
10.1016/S0921-8890(00)00087-7
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Static and dynamic scheduling methods have attracted a lot of attention in recent years. Among these, dynamic scheduling techniques handle scheduling problems where the scheduler does not possess detailed information about the jobs, which may arrive at the shop at any time. In this paper, an intelligent agent based dynamic scheduling system is proposed. It consists of two independent components: the agent and the simulated environment. The agent selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent. The agent is trained by an improved reinforcement learning algorithm through the learning stage and then it successively makes decisions to schedule the operations. (C) 2000 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：169 / 178
页数：10
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