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Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations

Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy
Department of Network Engineering, Universitat Politècnica de Catalunya (UPC), Esteve Terradas, 7, 08860 Castelldefels, Spain
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2020, 12(1), 257;
Received: 29 November 2019 / Revised: 17 December 2019 / Accepted: 26 December 2019 / Published: 28 December 2019
This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures. View Full-Text
Keywords: train scheduling; genetic algorithm; heuristic; MILP train scheduling; genetic algorithm; heuristic; MILP
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MDPI and ACS Style

Garrisi, G.; Cervelló-Pastor, C. Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations. Sustainability 2020, 12, 257.

AMA Style

Garrisi G, Cervelló-Pastor C. Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations. Sustainability. 2020; 12(1):257.

Chicago/Turabian Style

Garrisi, Gianmarco, and Cristina Cervelló-Pastor. 2020. "Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations" Sustainability 12, no. 1: 257.

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