Deep reinforcement learning for dynamic scheduling of a flexible job shop

• المؤلفون الرئيسيون: Liu, Renke, Piplani, Rajesh, Toro, Carlos
• مؤلفون آخرون: School of Mechanical and Aerospace Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Industrial engineering; Dynamic Scheduling; Flexible Job Shop
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/163903

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...