Closed-loop Rescheduling using Deep Reinforcement Learning

Closed-loop rescheduling using deep reinforcement learning is an approach to optimize scheduling decisions in a dynamic environment. In this approach, a deep reinforcement learning model is trained to make rescheduling decisions based on the current state of the system, such as machine status, job priority, and resource availability. The model then uses the feedback from the actual execution of the rescheduling decision to update its policy and improve its performance.

The closed-loop aspect of this approach means that the model is constantly learning and adapting to changes in the system, making it more robust and able to handle unforeseen events. This approach has been applied in various domains, such as manufacturing, logistics, and transportation, where scheduling decisions need to be made in real-time.

One of the advantages of using deep reinforcement learning for closed-loop rescheduling is that it can handle complex and dynamic environments, where traditional optimization techniques may not be effective. Additionally, the use of reinforcement learning allows the model to learn from experience and improve its performance over time.

Overall, closed-loop rescheduling using deep reinforcement learning is a promising approach for optimizing scheduling decisions in dynamic environments, and has the potential to improve efficiency and reduce costs in various industries.