This research presents a practical application of the Energy Efficient Train Control (EETC) problem, which involves a collaboration between the Operations Research group of the University of Bologna and ALSTOM Ferroviaria SpA. The work is carried out within the framework of project Swift, funded by the Emilia-Romagna regional authority. Given a train running on a certain line, the problem requires to determine a speed profile that minimizes the traction energy consumption. In particular, we consider the setting of a real-time application, in which the speed profile has to be recomputed due to changes in the schedule caused by unpredictable events. We introduce three solution methods: a constructive heuristic, a multi-start randomized constructive heuristic, and a Genetic Algorithm. Numerical experiments are performed on real-life instances. The results show that high quality solutions are produced and that the computing time is suitable for real-time applications.
Valentina Cacchiani, Antonio di Carmine, Giacomo Lanza, Michele Monaci, Federico Naldini, Luca Prezioso, et al. (2019). Energy-Efficient Train Control: A Practical Application. Cham : Springer International Publishing [10.1007/978-3-030-34960-8_6].
Energy-Efficient Train Control: A Practical Application
Valentina Cacchiani;Giacomo Lanza;Michele Monaci;Federico Naldini;Daniele Vigo
2019
Abstract
This research presents a practical application of the Energy Efficient Train Control (EETC) problem, which involves a collaboration between the Operations Research group of the University of Bologna and ALSTOM Ferroviaria SpA. The work is carried out within the framework of project Swift, funded by the Emilia-Romagna regional authority. Given a train running on a certain line, the problem requires to determine a speed profile that minimizes the traction energy consumption. In particular, we consider the setting of a real-time application, in which the speed profile has to be recomputed due to changes in the schedule caused by unpredictable events. We introduce three solution methods: a constructive heuristic, a multi-start randomized constructive heuristic, and a Genetic Algorithm. Numerical experiments are performed on real-life instances. The results show that high quality solutions are produced and that the computing time is suitable for real-time applications.File | Dimensione | Formato | |
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