Smart Manufacturing Scheduling with Edge Computing Using Multiclass Deep Q Network

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

209 Scopus citations


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.

Original languageEnglish
Article number8676376
Pages (from-to)4276-4284
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number7
StatePublished - 1 Jul 2019


  • Deep Q network
  • edge computing
  • job shop scheduling
  • multiple dispatching rules
  • smart manufacturing


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