Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

• URL: http://arxiv.org/abs/2604.23841v1
• Date: Sun, 26 Apr 2026 19:00:34 GMT
• Title: Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs
• Abstract summary: We introduce a unified graph framework that employs feature-based homogenization to project distinct node roles into a shared latent space.<n>This allows a standard homogeneous Graph Isomorphism Network to capture complex resource contention with linear complexity.<n>We show that our framework achieves state-of-the-art performance while exhibiting consistent zero-shot generalization.
• Score: 0.1274452325287335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 inference for large-scale industrial applications. Our empirical results demonstrate that our framework achieves state-of-the-art performance while exhibiting consistent zero-shot generalization. We identify the job-to-machine ratio as the primary driver of policy effectiveness, rather than absolute problem size. Based on this, we propose a hypothesis of structural saturation, demonstrating that policies trained on critically congested instances ($\mathcal{J} \approx \mathcal{M}$) learn scale-invariant resolution strategies. Agents trained at this saturation point internalize invariant conflict-resolution logic, allowing them to treat massive rectangular instances as a sequential concatenation of saturated sub-problems. This approach eliminates the need for expensive scale-specific retraining and prevents overfitting to statistical shortcuts, providing a robust and efficient pathway for deploying RL solutions in dynamic production environments.

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