Scalable Production Scheduling via Unified Homogeneous Graphs

A recently developed unified graph framework for the Job Shop Scheduling Problem (JSSP) demonstrates linear complexity alongside cutting-edge performance. By employing feature-based homogenization, this framework maps various node roles into a common latent space, allowing a conventional homogeneous Graph Isomorphism Network to effectively address intricate resource contention. This innovative strategy resolves the scalability issues faced by current Reinforcement Learning models, which typically encounter quadratic graph complexity or heterogeneous layer overhead. Empirical findings indicate reliable zero-shot generalization, with the job-to-machine ratio recognized as the key factor influencing policy performance. The approach guarantees low-latency inference suitable for extensive industrial applications.