Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach
This work addresses real-time scheduling challenges in industrial settings, representing an incremental improvement over existing deep reinforcement learning methods.
The paper tackled the flexible job-shop scheduling problem by introducing an enhanced deep reinforcement learning approach using heterogeneous graph neural networks and novel techniques like diverse policy generation and dispatching rule integration, achieving superior results compared to state-of-the-art methods, especially for large instances.
In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is essential. Recent methods propose the use of deep reinforcement learning (DRL) to learn policies capable of generating solutions under this constraint. The objective of this paper is to introduce a new DRL method for solving the flexible job-shop scheduling problem, particularly for large instances. The approach is based on the use of heterogeneous graph neural networks to a more informative graph representation of the problem. This novel modeling of the problem enhances the policy's ability to capture state information and improve its decision-making capacity. Additionally, we introduce two novel approaches to enhance the performance of the DRL approach: the first involves generating a diverse set of scheduling policies, while the second combines DRL with dispatching rules (DRs) constraining the action space. Experimental results on two public benchmarks show that our approach outperforms DRs and achieves superior results compared to three state-of-the-art DRL methods, particularly for large instances.