| Dataset Name | SOTA Method | Metric | Trend | ||
|---|---|---|---|---|---|
| VRP 100 Customers (100 instances) | HGS-PYVRP | Objective Value15.5 | 28 | 1mo ago | |
| VRP Variant Specific (n=50, 100) 1K 1 (test) | Objective Value5.745 | 25 | 4d ago | ||
| OCVRP 48 standard 100-node benchmark instances | HGS-PyVRP | Objective Value9.72 | 18 | 1mo ago | |
| OVRP n=100 | RF-TE | Time (m)0.15 | 17 | 1mo ago | |
| VRP 500 Customers (100 instances) | HGS-PYVRP | Objective Value36.84 | 16 | 1mo ago | |
| OCVRP n=100 | HGS-PyVRP | Objective Value9.72 | 12 | 1mo ago | |
| CVRP 48 standard 100-node benchmark instances | HGS-PyVRP | Objective Value15.62 | 12 | 1mo ago | |
| VRPB n=50 | POMO | Computation Time (min)0.085 | 12 | 1mo ago | |
| CVRP n=50 | RF-POMO | Computation Time (m)0.03 | 12 | 1mo ago | |
| HFVRPTW (N=100, K=30) | PyVRP | Objective Value9.37 | 10 | 11d ago | |
| HFVRPL N=100 K=30 | PyVRP | Objective Value5.83 | 10 | 11d ago | |
| HFVRPB N=100, K=30 | PyVRP | Objective Value5.51 | 10 | 11d ago | |
| HFOVRP N=100, K=30 | PyVRP | Objective Value4.08 | 10 | 11d ago | |
| HFCVRP N=100, K=30 | PyVRP | Objective Value5.64 | 10 | 11d ago | |
| Vehicle Routing Problem | SGE | Cost Improvement % vs IO71.92 | 8 | 1mo ago | |
| OCVRPBLTW n=100 | HGS-PyVRP | Obj Value19.16 | 6 | 1mo ago | |
| OCVRPBLTW n=50 | HGS-PyVRP | Objective Value11.67 | 6 | 1mo ago | |
| OCVRPBTW n=100 | HGS-PyVRP | Objective Value19.16 | 6 | 1mo ago | |
| OCVRPBTW n=50 | HGS-PyVRP | Objective Value11.67 | 6 | 1mo ago | |
| OCVRPBL n=100 | HGS-PyVRP | Objective Value10.34 | 6 | 1mo ago | |
| OCVRPBL n=50 | HGS-PyVRP | Objective Value6.9 | 6 | 1mo ago | |
| CVRPBLTW n=100 | HGS-PyVRP | Objective Value29.03 | 6 | 1mo ago | |
| CVRPBLTW n=50 | HGS-PyVRP | Objective Value18.36 | 6 | 1mo ago | |
| OCVRPB n=100 | HGS-PyVRP | Objective Value10.34 | 6 | 1mo ago | |
| OCVRPB n=50 | HGS-PyVRP | Objective Value6.9 | 6 | 1mo ago |
