PDScheduler: Persistent Dynamic Policy Decomposition for Flexible Job-Shop Scheduling

Despite rapid progress in neural methods for the Flexible Job Shop Scheduling Problem (FJSP), existing paradigms face two key bottlenecks as instances grow: two stage approaches commit to machine assignments prematurely, rendering the downstream sequencing search prohibitively expensive, while autoregressive methods face decision horizons that grow with problem scale, making long horizon reasoning difficult and inference costly. To address these challenges, we propose PDScheduler, a neural scheduling framework built upon Persistent Dynamic Policy Decomposition (PDPD), which factorizes scheduling guidance into a persistent component and a dynamic residual. Specifically, PDScheduler employs masked discrete diffusion to generate a global assignment heatmap once per instance, while a lightweight residual actor dynamically adjusts the persistent guidance according to the evolving partial schedule. This design performs the heavy computation over the instance only once and shares the resulting global guidance across all subsequent decisions, eliminating the redundant recomputation that burdens existing constructive methods. Experiments on public FJSP benchmarks show that PDScheduler outperforms state of the art neural baselines in makespan under both in distribution and out of distribution evaluations while running 1.5× faster, demonstrating its superior solution quality and computational efficiency.