Industrial Scheduling on Industrial Scenario (test)
89.15PITMD (%)TempoNet
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| TempoNet2026.02 | 89.15 | 12.43 | 0.42 | |
| GNN-based2026.02 | 88.8 | 13.2 | 0.43 | |
| LSTM-PPO-Based2026.02 | 88.5 | 13 | 0.44 | |
| Transformer-based DRL2026.02 | 88.2 | 13.5 | 0.46 | |
| Transformer-based2026.02 | 88 | 14.2 | 0.45 | |
| ENF-S2026.02 | 87.5 | 14 | 0.47 | |
| DIOS2026.02 | 87.28 | 16.72 | — | |
| HQIGA2026.02 | 85.7 | 16.34 | 0.52 | |
| Deep reinforcement learning-based2026.02 | 85 | 15 | 0.5 | |
| Multi-Core Particle Swarm2026.02 | 84 | 16 | 0.55 | |
| Mo-QIGA2026.02 | 83.21 | 15.11 | 0.48 | |
| EDF2026.02 | 20.81 | 20.68 | — | |
| FCFS2026.02 | 9.83 | 21.2 | — |