2026-05-30T10:55:41Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/275302026-02-03T12:16:23Zcom_10630_2254col_10630_37959

Cintrano López, Christian Toutouh-el-Alamin, Jamal 2023-09-15T10:44:34Z 2023-09-15T10:44:34Z 2022 https://hdl.handle.net/10630/27530 This article presents a multiobjective variation of the problem of locating electric vehicle charging stations (EVCS) in a city known as the Multiobjective Electric Vehicle Charging Stations Locations (MO-EVCS-L) problem. MO-EVCS-L considers two conflicting objectives: maximizing the quality of service of the charging station network and minimizing the deployment cost when installing different types of charging stations. Two multiobjective metaheuristics are proposed to address MO-EVCS-L: the Non-dominated Sorting Genetic Algorithm, version II (NSGA-II) and the Strength Pareto Evolutionary Algorithm, version 2 (SPEA2). The experimental analysis is performed on a real-world case study defined in Malaga, Spain, and it compares the proposed approaches with a baseline algorithm. Results show that the SPEA2 computes the most competitive solutions, even though both metaheuristics found an accurate set of solutions that provide different trade-offs between the quality of service and the installation costs. eng open access Transporte - Aspectos ambientales Desarrollo sostenible Vehículos eléctricos Multiobjective electric vehicle charging station locations in a city scale area: Malaga study case. conference output