Research5h ago
Transformer Model Tackles Open Shop Scheduling
A new encoder-decoder architecture trained on Taillard benchmark instances produces schedules within 15-30% of best-known makespans. The system uses deep reinforcement learning to map processing-time matrices to feasible schedules. This approach bypasses the manual tuning required by classical dispatching rules. It offers a scalable, though imperfect, alternative for industrial scheduling.