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.
2019
Advances in Optimization and Decision Science for Society, Services and Enterprises
57
68
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].
Valentina Cacchiani; Antonio di Carmine; Giacomo Lanza; Michele Monaci; Federico Naldini; Luca Prezioso; Rosalba Suffritti; Daniele Vigo
File in questo prodotto:
File Dimensione Formato  
postprint_AIRO.pdf

Open Access dal 27/01/2021

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 263.86 kB
Formato Adobe PDF
263.86 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/763565
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact