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.

翻译：高效解决实际工业应用中的作业车间调度问题，需要兼具计算精简性与拓扑鲁棒性的策略。尽管强化学习在自动化调度规则方面展现出潜力，但现有模型常受限于二次图复杂度或异构层架构开销造成的可扩展性瓶颈。我们提出一种统一图框架，通过基于特征的均质化方法将不同节点角色映射至共享潜在空间。这使得标准同构图同构网络能以线性复杂度捕捉复杂的资源竞争关系，确保大规模工业应用中的低延迟推理。实验结果表明，我们的框架在实现最先进性能的同时，展现出一致的零样本泛化能力。我们提出作业与机器比例是影响策略有效性的核心要素，而非问题的绝对规模。基于此，我们构建结构饱和假说，证明在临界拥挤实例（$\mathcal{J} \approx \mathcal{M}$）上训练的策略能学习尺度不变的分辨策略。在此饱和点训练的智能体内化冲突解决逻辑的不变性，可将大规模矩形实例视为饱和子问题的顺序拼接。该方法无需昂贵的规模特定重训练，并避免对统计捷径的过拟合，为动态生产环境中部署强化学习解决方案提供了稳健高效的路径。