Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

TL;DR

This paper presents a scalable, linear-complexity graph-based reinforcement learning framework for job shop scheduling, enabling efficient, generalizable policies suitable for large industrial systems.

Contribution

It introduces a unified homogeneous graph approach with feature-based homogenization, achieving state-of-the-art performance and zero-shot generalization in production scheduling.

Findings

Linear complexity graph model captures resource contention efficiently.

Policies trained on congested instances generalize well to larger problems.

Structural saturation hypothesis explains scale-invariant policy effectiveness.

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.