Variable settings and assessment measures for 27 random MNCP problems
| Variables | MIP | Heuristic | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| reqts|I| | locs | P U D| | days |V| | Time (sec) | Time (sec) | Theater_Eroad (%) | Theater_Erail (%) | Theater_E (%) | Node_Eroad (%) | Node_Erail (%) | Peak_Eroad (%) | Peak_Erail (%) | Time delta (%) | |
| 1 | 100 | 10 | 50 | 23 | 18 | 1.9 | 1.8 | 1.8 | 2.0 | 1.8 | 56.4 | 45.5 | −20.9 |
| 2 | 100 | 10 | 100 | 85 | 18 | 1.8 | 1.6 | 1.7 | 1.8 | 1.6 | 20.6 | 18.8 | −78.8 |
| 3 | 100 | 10 | 200 | 321 | 18 | 1.8 | 1.6 | 1.7 | 1.9 | 1.6 | 14.2 | 23.9 | −94.4 |
| 4 | 100 | 30 | 50 | 183 | 18 | 1.9 | 1.7 | 1.8 | 1.8 | 1.7 | 14.9 | 2.3 | −89.9 |
| 5 | 100 | 30 | 100 | 721 | 19 | 1.5 | 2.2 | 1.9 | 1.5 | 2.1 | 0.0 | 0.0 | −97.4 |
| 6 | 100 | 30 | 200 | 2837 | 20 | 1.9 | 1.5 | 1.7 | 1.9 | 1.6 | 0.0 | 0.0 | −99.3 |
| 7 | 100 | 50 | 50 | 510 | 18 | 2.2 | 1.8 | 2.0 | 2.1 | 1.9 | 0.6 | 0.0 | −96.4 |
| 8 | 100 | 50 | 100 | 1985 | 21 | 1.7 | 1.8 | 1.8 | 1.7 | 1.7 | 0.0 | 0.0 | −99.0 |
| 9 | 100 | 50 | 200 | 7740 | 21 | 1.5 | 1.8 | 1.7 | 1.3 | 1.8 | 0.0 | 0.0 | −99.7 |
| 10 | 300 | 10 | 50 | 68 | 50 | 1.9 | 1.8 | 1.8 | 1.9 | 1.8 | 77.4 | 68.4 | −26.2 |
| 11 | 300 | 10 | 100 | 257 | 53 | 1.9 | 1.6 | 1.7 | 1.8 | 1.5 | 67.4 | 67.8 | −79.3 |
| 12 | 300 | 10 | 200 | 982 | 52 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 37.7 | 37.2 | −94.7 |
| 13 | 300 | 30 | 50 | 553 | 52 | 1.8 | 1.9 | 1.8 | 1.9 | 1.9 | 47.0 | 58.4 | −90.6 |
| 14 | 300 | 30 | 100 | 2160 | 61 | 1.8 | 1.6 | 1.7 | 1.8 | 1.6 | 17.1 | 28.6 | −97.2 |
| 15 | 300 | 30 | 200 | 8567 | 60 | 1.7 | 2.0 | 1.8 | 1.6 | 1.9 | 15.6 | 14.1 | −99.3 |
| 16 | 300 | 50 | 50 | 1514 | 59 | 1.8 | 1.8 | 1.8 | 1.9 | 1.7 | 33.2 | 33.9 | −96.1 |
| 17 | 300 | 50 | 100 | 5955 | 56 | 1.9 | 1.7 | 1.8 | 1.8 | 1.7 | 16.7 | 14.8 | −99.1 |
| 18 | 300 | 50 | 200 | 23722 | 65 | 1.7 | 1.9 | 1.8 | 1.6 | 1.7 | 0.0 | 12.5 | −99.7 |
| 19 | 500 | 10 | 50 | 113 | 99 | 1.8 | 1.9 | 1.8 | 1.9 | 1.8 | 69.9 | 64.1 | −12.1 |
| 20 | 500 | 10 | 100 | 420 | 102 | 1.8 | 1.7 | 1.8 | 1.8 | 1.7 | 98.2 | 70.5 | −75.7 |
| 21 | 500 | 10 | 200 | 1631 | 97 | 1.9 | 1.7 | 1.8 | 2.0 | 1.7 | 63.5 | 66.6 | −94.1 |
| 22 | 500 | 30 | 50 | 904 | 90 | 1.8 | 1.7 | 1.7 | 1.7 | 1.7 | 64.0 | 60.1 | −90.0 |
| 23 | 500 | 30 | 100 | 3588 | 98 | 1.9 | 1.7 | 1.8 | 2.0 | 1.7 | 39.8 | 49.3 | −97.3 |
| 24 | 500 | 30 | 200 | 14277 | 102 | 1.7 | 1.8 | 1.7 | 1.7 | 1.8 | 24.9 | 17.2 | −99.3 |
| 25 | 500 | 50 | 50 | 2502 | 103 | 1.7 | 1.7 | 1.7 | 1.7 | 1.6 | 54.3 | 49.7 | −95.9 |
| 26 | 500 | 50 | 100 | 9857 | 103 | 1.7 | 1.7 | 1.7 | 1.7 | 1.7 | 27.0 | 24.8 | −99.0 |
| 27 | 500 | 50 | 200 | 39310 | 108 | 1.7 | 1.9 | 1.8 | 1.7 | 2.0 | 11.1 | 13.9 | −99.7 |
| Variables | MIP | Heuristic | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Time (sec) | Time (sec) | ||||||||||||
| 1 | 100 | 10 | 50 | 23 | 18 | 1.9 | 1.8 | 1.8 | 2.0 | 1.8 | 56.4 | 45.5 | −20.9 |
| 2 | 100 | 10 | 100 | 85 | 18 | 1.8 | 1.6 | 1.7 | 1.8 | 1.6 | 20.6 | 18.8 | −78.8 |
| 3 | 100 | 10 | 200 | 321 | 18 | 1.8 | 1.6 | 1.7 | 1.9 | 1.6 | 14.2 | 23.9 | −94.4 |
| 4 | 100 | 30 | 50 | 183 | 18 | 1.9 | 1.7 | 1.8 | 1.8 | 1.7 | 14.9 | 2.3 | −89.9 |
| 5 | 100 | 30 | 100 | 721 | 19 | 1.5 | 2.2 | 1.9 | 1.5 | 2.1 | 0.0 | 0.0 | −97.4 |
| 6 | 100 | 30 | 200 | 2837 | 20 | 1.9 | 1.5 | 1.7 | 1.9 | 1.6 | 0.0 | 0.0 | −99.3 |
| 7 | 100 | 50 | 50 | 510 | 18 | 2.2 | 1.8 | 2.0 | 2.1 | 1.9 | 0.6 | 0.0 | −96.4 |
| 8 | 100 | 50 | 100 | 1985 | 21 | 1.7 | 1.8 | 1.8 | 1.7 | 1.7 | 0.0 | 0.0 | −99.0 |
| 9 | 100 | 50 | 200 | 7740 | 21 | 1.5 | 1.8 | 1.7 | 1.3 | 1.8 | 0.0 | 0.0 | −99.7 |
| 10 | 300 | 10 | 50 | 68 | 50 | 1.9 | 1.8 | 1.8 | 1.9 | 1.8 | 77.4 | 68.4 | −26.2 |
| 11 | 300 | 10 | 100 | 257 | 53 | 1.9 | 1.6 | 1.7 | 1.8 | 1.5 | 67.4 | 67.8 | −79.3 |
| 12 | 300 | 10 | 200 | 982 | 52 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 37.7 | 37.2 | −94.7 |
| 13 | 300 | 30 | 50 | 553 | 52 | 1.8 | 1.9 | 1.8 | 1.9 | 1.9 | 47.0 | 58.4 | −90.6 |
| 14 | 300 | 30 | 100 | 2160 | 61 | 1.8 | 1.6 | 1.7 | 1.8 | 1.6 | 17.1 | 28.6 | −97.2 |
| 15 | 300 | 30 | 200 | 8567 | 60 | 1.7 | 2.0 | 1.8 | 1.6 | 1.9 | 15.6 | 14.1 | −99.3 |
| 16 | 300 | 50 | 50 | 1514 | 59 | 1.8 | 1.8 | 1.8 | 1.9 | 1.7 | 33.2 | 33.9 | −96.1 |
| 17 | 300 | 50 | 100 | 5955 | 56 | 1.9 | 1.7 | 1.8 | 1.8 | 1.7 | 16.7 | 14.8 | −99.1 |
| 18 | 300 | 50 | 200 | 23722 | 65 | 1.7 | 1.9 | 1.8 | 1.6 | 1.7 | 0.0 | 12.5 | −99.7 |
| 19 | 500 | 10 | 50 | 113 | 99 | 1.8 | 1.9 | 1.8 | 1.9 | 1.8 | 69.9 | 64.1 | −12.1 |
| 20 | 500 | 10 | 100 | 420 | 102 | 1.8 | 1.7 | 1.8 | 1.8 | 1.7 | 98.2 | 70.5 | −75.7 |
| 21 | 500 | 10 | 200 | 1631 | 97 | 1.9 | 1.7 | 1.8 | 2.0 | 1.7 | 63.5 | 66.6 | −94.1 |
| 22 | 500 | 30 | 50 | 904 | 90 | 1.8 | 1.7 | 1.7 | 1.7 | 1.7 | 64.0 | 60.1 | −90.0 |
| 23 | 500 | 30 | 100 | 3588 | 98 | 1.9 | 1.7 | 1.8 | 2.0 | 1.7 | 39.8 | 49.3 | −97.3 |
| 24 | 500 | 30 | 200 | 14277 | 102 | 1.7 | 1.8 | 1.7 | 1.7 | 1.8 | 24.9 | 17.2 | −99.3 |
| 25 | 500 | 50 | 50 | 2502 | 103 | 1.7 | 1.7 | 1.7 | 1.7 | 1.6 | 54.3 | 49.7 | −95.9 |
| 26 | 500 | 50 | 100 | 9857 | 103 | 1.7 | 1.7 | 1.7 | 1.7 | 1.7 | 27.0 | 24.8 | −99.0 |
| 27 | 500 | 50 | 200 | 39310 | 108 | 1.7 | 1.9 | 1.8 | 1.7 | 2.0 | 11.1 | 13.9 | −99.7 |
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