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
@inproceedings{DamLiu:23-037,
author = {Daman, Alexander and Liu, Xiaoyu and Dabiri, Azita and De
Schutter, Bart},
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}
}