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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...
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.
This study systematically investigates the conditions under which joint training is essential for optimal performance in the job-shop scheduling problem with transportation resources, and quantifies the coordination gap - the performance difference between these two training modalities.
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