Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs
Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust. While Reinforcement Learning has shown potential in automating dispatching rules, existing models often struggle with a scalability bottleneck caused by quadratic graph complexity or the architectural overhead of heterogeneous layers. We introduce a unified graph framework that employs feature-based homogenization to project distinct node roles into a shared latent space. This allows a standard homogeneous Graph Isomorphism Network to capture complex resource contention with linear complexity, ensuring low-latency inferenc...
| Authors | Jonathan Hoss, Moritz Link, Noah Klarmann |
|---|---|
| Primary category | cs.LG |
| Published | 26 April 2026 |
| Last updated at source | 12 June 2026 |
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