arXiv AI September 28, 2026 By Mateo Toro Diz, Jonathan Hoss, Noah Klarmann

PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem

The paper introduces PORL, a hybrid method that first trains a general scheduling policy through online reinforcement learning in simulation, then fine‑tunes it offline on production data using a KL‑divergence constraint to limit policy drift. PORL is evaluated on Job Shop Scheduling Problem instances with distribution shifts and various data sources, consistently outperforming standalone offline RL and other baselines, especially when offline data quality is low. The results suggest that offline adaptation of pretrained policies can improve industrial scheduling when direct online exploration is impractical.

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