Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 7; pp. 4276 - 4284
• Main Authors: Lin, Chun-Cheng, Deng, Der-Jiunn, Chih, Yen-Ling, Chiu, Hsin-Ting
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer simulation; Decisions; Deep Q network; Dispatching; Edge computing; Factories; HyperText Markup Language; Industrial plants; Job shop scheduling; Job shops; Machine learning; Manufacturing; multiple dispatching rules; Production facilities; Production scheduling; Response time; Servers; Smart manufacturing; Task analysis
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2908210

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.