Research Article
Train Scheduling Optimization for an Urban Rail Transit Line: A Simulated-Annealing Algorithm Using a Large Neighborhood Search Metaheuristic
Table 3
Comparison of values under an optimized irregular schedule and the current regular schedule.
| Station index | Length (m) | DT1 (s) | DT2 (s) | Gap1 (%) | LR1 | LR2 | Gap3 (%) |
| 1 | 657 | 40 | 38 | −5.0 | 0.173 | 0.171 | −1.2 | 2 | 1361 | 30 | 28 | −6.7 | 0.380 | 0.375 | −1.3 | 3 | 857 | 35 | 32 | −8.6 | 0.506 | 0.498 | −1.6 | 4 | 1425 | 35 | 34 | −2.9 | 0.578 | 0.561 | −2.9 | 5 | 1602 | 55 | 58 | +5.5 | 0.872 | 0.844 | −3.2 | 6 | 1744 | 30 | 29 | −3.3 | 0.818 | 0.789 | −3.5 | 7 | 1146 | 50 | 48 | −4.0 | 0.802 | 0.775 | −3.4 | 8 | 1074 | 30 | 26 | −13.3 | 0.774 | 0.746 | −3.6 | 9 | 1303 | 35 | 33 | −5.7 | 0.749 | 0.725 | −3.2 | 10 | 1587 | 45 | 42 | −6.7 | 0.647 | 0.629 | −2.8 | 11 | 1183 | 30 | 29 | −3.3 | 0.632 | 0.613 | −3.0 | 12 | 1085 | 30 | 28 | −6.7 | 0.625 | 0.606 | −3.0 | 13 | 874 | 30 | 30 | 0 | 0.588 | 0.569 | −3.2 | 14 | 1199 | 45 | 41 | −8.9 | 0.605 | 0.587 | −3.0 | 15 | 860 | 30 | 29 | −3.3 | 0.593 | 0.574 | −3.2 | 16 | 1023 | 30 | 28 | −6.7 | 0.560 | 0.541 | −3.4 | 17 | 1435 | 40 | 37 | −7.5 | 0.541 | 0.523 | −3.3 | 18 | 1908 | 30 | 27 | −10.0 | 0.487 | 0.472 | −3.1 | 19 | 6924 | 50 | 46 | −8.0 | 0.629 | 0.611 | −2.9 | 20 | 1784 | 35 | 33 | −5.7 | 0.507 | 0.489 | −3.6 | 21 | 3361 | 25 | 23 | −8.0 | 0.455 | 0.440 | −3.3 | 22 | 1894 | 30 | 29 | −3.3 | 0.401 | 0.389 | −3.0 | 23 | 1540 | 40 | 35 | −12.5 | 0.329 | 0.319 | −3.0 | 24 | 5895 | 30 | 28 | −6.7 | 0.186 | 0.181 | −2.7 | 25 | 437 | 40 | 37 | −7.5 | — | — | — | SUM | 44158 | 900 | 848 | −5.8 | — | — | — |
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