Train rescheduling and platforming in large high-speed railway stations

To deal with train delays in large high-speed railway stations, a multi-objective mixed-integer nonlinear programming (MO-MINLP) optimization model was proposed. The model used the arrival time, departure time, track occupation, and route selection as the decision variables, and fully considered the...

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Veröffentlicht in:International Journal of Transportation Science and Technology Jg. 16; S. 100 - 118
Hauptverfasser: Teng, Jing, Gao, Jinke, Wang, Pengling, Qu, Siyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2024
KeAi Communications Co., Ltd
Schlagworte:
Large high-speed railway station
Multiple objective mixed-integer nonlinear programming (MO-MINLP)
Rescheduling and platforming
Rolling horizon
Rescheduling and platforming
Multiple objective mixed-integer nonlinear programming (MO-MINLP)
Large high-speed railway station
Rolling horizon
ISSN:2046-0430
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Zusammenfassung:To deal with train delays in large high-speed railway stations, a multi-objective mixed-integer nonlinear programming (MO-MINLP) optimization model was proposed. The model used the arrival time, departure time, track occupation, and route selection as the decision variables, and fully considered the station infrastructure layout, train operational requirements, and time standards as limiting factors. The optimization objectives were to minimize train delays and reduce track and to route adjustments. To realize the large-scale and rapid solution of the MO-MINLP model, this study proposed a rolling horizon optimization algorithm that used half an hour as a time interval and solved the rescheduling and platforming problem of each time interval step-by-step. In numerical experiments, 227 train movements under delay circumstances in Hangzhoudong station were optimized by using the proposed model and solution algorithm. The results show that the proposed MO-MINLP model could resolve route conflicts, compress unnecessary dwell times, and reduce train delays, and the solution algorithm could efficiently increase the computational speed. The maximum solution time for optimizing the 227 train movements is 15 min 24 s.
ISSN:2046-0430
DOI:10.1016/j.ijtst.2023.11.001