A Comparative Study of Online versus Offline Machine Shop Scheduling Methods

Implementing complex online or offline scheduling solutions for machine shop scheduling is often difficult in practice, as these methods can be computationally expensive and difficult to understand. To provide a transition to these more complex solutions, this paper compares our proposed scheduling method based on a multi-agent system with state-of-the-art traditional offline and reinforcement learning-based online methods. Computational results indicate that our method is comparable to the existing complex offline and online methods, while computationally more advantageous, showcasing its effectiveness and flexibility.