Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

2026-04-26

AI Summary

This work addresses the scalability limitations of reinforcement learning approaches in job shop scheduling, which often arise from complex or heterogeneous graph structures. To overcome this, the authors propose a homogeneous graph representation based on feature homogenization, mapping both jobs and machines into a shared latent space and enabling a graph neural network framework with linear complexity. By introducing a structural saturation hypothesis, they reveal how the ratio of jobs to machines influences policy effectiveness and demonstrate that agents trained on critically congested instances can zero-shot generalize to larger-scale problems. Integrating a Graph Isomorphism Network (GIN) with reinforcement learning, the method achieves state-of-the-art performance while maintaining low inference latency, significantly enhancing deployment feasibility in large-scale dynamic production environments.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Job Shop Scheduling
• Scalability
• Graph Complexity
• Reinforcement Learning
• Production Scheduling

Innovation

• unified homogeneous graphs
• linear complexity
• structural saturation
• zero-shot generalization
• job shop scheduling