@article{3024129,
    title = "Multi-objective Optimization of Train Speed Profiles Using History Measurements",
    author = "Achilleos, A. and Anastasopoulos, M. and Tzanakaki, A. and Iordache, M. and Langlois, O. and Pheulpin, J.-F. and Simeonidou, D.",
    journal = "Communications in Computer and Information Science",
    year = "2021",
    volume = "1217",
    pages = "363-374",
    publisher = "Springer Science and Business Media Deutschland GmbH",
    doi = "10.1007/978-3-030-68028-2_17",
    keywords = "Data envelopment analysis;  Energy utilization;  Green computing;  Intelligent systems;  Intelligent vehicle highway systems;  Railroad transportation;  Smart city;  Traffic control, Model framework;  Multi-objective optimization scheme;  Nonparametric methods;  Numerical results;  Railway system;  Scheduling constraints;  Train speed, Multiobjective optimization",
    abstract = "The present study focuses on the development of a multi-objective optimization scheme to improve the efficiency of railway systems. This is achieved through the identification of the optimal train speed profiles employing a novel modeling framework based on Data Envelopment Analysis (DEA). Train speed profiles are selected with the objective to transfer more passengers in less time and with less energy under scheduling constraints. Given that DEA is a data oriented, non-parametric method, a large-scale experimental camping has been carried out over an operational tramway system to collect the required inputs/outputs. Numerical results show that when the proposed approach energy consumption can be reduced by 10%. © 2021, Springer Nature Switzerland AG."
}