To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …

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 …

This paper proposes a novel intelligent scheduling method based on deep reinforcement
learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) …

Production scheduling has a significant role when optimizing production objectives such as
production efficiency, resource utilization, cost control, energy-saving, and emission …

Robot-assisted screw removal can greatly facilitate the disassembly and remanufacturing
automation of end-of-life products to realise circular economies. However, it is challenging to …

Dynamic event disturbances during production such as reworking of defective operations
more challenge scheduling optimization. Efficient response to operation reworking is crucial …

In this study, we investigate a real-time system where computationally intensive tasks are
executed using cloud computing platforms in data centers. These data centers are designed …

Abstract The Apparent Tardiness Cost (ATC) dispatching rule was initially developed to
minimize tardiness in single-machine scheduling problems. ATC extensions have been …

Research on deep reinforcement learning (DRL) based production scheduling (PS) has
gained a lot of attention in recent years, primarily due to the high demand for optimizing …

Reconfigurable manufacturing system is considered as a promising next-generation
manufacturing paradigm. However, limited equipment and complex product processes add …