dailyai.report

A new Transformer-based policy solves open shop scheduling problems using an encoder-decoder architecture. Trained on Taillard benchmark instances, the model produces feasible schedules within 15-30% of best-known makespans. It replaces manual tuning of classical dispatching rules with deep reinforcement learning. This approach offers a scalable alternative for industrial scheduling tasks.