Research Article
Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours
Table 3
Performance analysis of CPLEX and the clustering algorithm. K = 5.
| N | CPLEX | Our method | Obj. | CPU(s) | Avg. Obj. | Avg. gap | Best Obj. | Best gap | Avg. CPU(s) |
| 30 | 15 | 64.231 | 14.6 | 2.67 | 15 | 0.00 | 38.841 | 50 | 32 | 650.289 | 30.8 | 3.75 | 31 | 3.13 | 43.142 | 70 | 49 | 2275.631 | 47.1 | 3.88 | 48 | 2.04 | 54.260 | 90 | 68 | 7200 | 65.3 | 3.97 | 66 | 2.94 | 60.394 | 110 | 85 | 7200 | 81.5 | 4.12 | 82 | 3.53 | 69.787 | 150 | 114 | 7200 | 108.1 | 5.18 | 109 | 4.39 | 81.140 | 190 | | 7200 | 149.5 | | 153 | | 89.769 | 230 | | 7200 | 176.8 | | 190 | | 96.145 |
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Avg. Obj: average objective value; Best Obj: best objective value; Avg. CPU: average CPU time after 100 runs.
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