Mateo Toro Diz

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...