Author

Jonathan Hoss

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#artificial intelligence Preprint Sep 2026

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

The Job Shop Scheduling Problem (JSSP) is a fundamental combinatorial optimization problem in industrial optimization. This work introduces Pretrained Offline Reinforcement Learning (PORL), a hybrid approach that combines simulation-based online pretraining with offline fine-tuning on production-specific data. Reinforc...

Mateo Toro Diz, Jonathan Hoss, Noah Klarmann · 0 citations

#artificial intelligence Preprint Sep 2026

Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling

Curriculum learning for graph neural network-based reinforcement learning in the job shop scheduling problem is compared with single-size training across three target sizes, showing that curriculum learning consistently reduces wall-clock training time, with larger benefits as the target size increases.

J. K. Vasudevan, Jonathan Hoss, Noah Klarmann · 0 citations