Benders decomposition-based optimization of train departure frequencies in metro networks


Reference:

A. Daman, X. Liu, A. Dabiri, and B. De Schutter, "Benders decomposition-based optimization of train departure frequencies in metro networks," Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, pp. 5371-5376, Sept. 2023.

Abstract:

Timetables determine the service quality for passengers and the energy consumption of trains in metro systems. In metro networks, a timetable can be made by designing train departure frequencies for different periods of the day, which is typically formulated as a mixed-integer linear programming (MILP) problem. In this paper, we first apply Benders decomposition to optimize the departure frequencies considering time-varying passenger origin-destination demands in metro networks. An ε-optimal Benders decomposition approach is subsequently used to reduce the solution time further. The performance of both methods is illustrated in a simulation-based case study using a grid metro network. The results show that both the classical Benders decomposition approach and the ε-optimal Benders decomposition approach can significantly reduce the computation time for the optimization of train departure frequencies in metro networks. In addition, the ε-optimal Benders decomposition approach can further reduce the solution time compared to the classical Benders decomposition approach when the problem scale increases while maintaining an acceptable level of performance.

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Bibtex entry:

@inproceedings{DamLiu:23-037,
author={A. Daman and X. Liu and A. Dabiri and B. {D}e Schutter},
title={Benders decomposition-based optimization of train departure frequencies in metro networks},
booktitle={Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
address={Bilbao, Spain},
pages={5371--5376},
month=sep,
year={2023},
doi={10.1109/ITSC57777.2023.10422588}
}


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