@misc{10498/35436,
year = {2023},
url = {http://hdl.handle.net/10498/35436},
abstract = {An efficient public transport system is essential for sustainable city development, as it directly affects people’s welfare. This article addresses the urban public transport timetabling problem with multi-objective evolutionary algorithms, considering multiple vehicle types and respecting the public transport restrictions of local authorities. The conflicting objectives are the minimization of fuel consumption and unsatisfied user demand, which are essential to make transit buses an attractive alternative for users, thus promoting environmentally friendly mobility. The problem was solved with two well-known metaheuristics, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and cellular genetic algorithm for multi-objective optimization (MOCell), and their performance was compared using several metrics. Their parameters were tuned with a thorough study, and several evolutionary operators designed for the problem were considered. The outcomes suggest that a solution using various types of buses can produce diverse dispatching strategies, reducing pollutant emissions and maintaining tolerable ridership losses.},
organization = {This project was partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades and the ERDF [contract RTI2018-100754-B-I00] (iSUN project), ERDF [project FEDER-UCA18-108393] (OPTIMALE), and Junta de Andalucía and ERDF (GENIUS) [project P18-2399].},
publisher = {Taylor & Francis},
keywords = {Public transport},
keywords = {sustainable cities},
keywords = {greenhouse gas emission},
keywords = {multi-objective optimization},
keywords = {evolutionary algorithms},
title = {A novel multi-objective optimization approach to guarantee quality of service and energy efficiency in a heterogeneous bus fleet system},
doi = {10.1080/0305215X.2022.2055007},
author = {Peña, David and Tchernykh, Andrei and Dorronsoro Díaz, Bernabé and Ruiz Villalobos, Patricia},
}