Deep reinforcement learning for dynamic scheduling of a flexible job shop

• Main Authors: Liu, Renke, Piplani, Rajesh, Toro, Carlos
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Industrial engineering; Dynamic Scheduling; Flexible Job Shop
• Online Access: https://hdl.handle.net/10356/163903
• Institution: Nanyang Technological University

The ability to handle unpredictable dynamic events is becoming more important in pursuing agile and flexible production scheduling. At the same time, the cyber-physical convergence in production system creates massive amounts of industrial data that needs to be mined and analysed in real-time. To fa...