Smart Manufacturing Scheduling with Edge Computing Using Multiclass Deep Q Network

Chun-Cheng Lin, Der Jiunn Deng*, Yen Ling Chih, Hsin Ting Chiu

*此作品的通信作者

研究成果: Article同行評審

193 引文 斯高帕斯(Scopus)

摘要

Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

原文English
文章編號8676376
頁(從 - 到)4276-4284
頁數9
期刊IEEE Transactions on Industrial Informatics
15
發行號7
DOIs
出版狀態Published - 1 7月 2019

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