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Profesor guíadc.contributor.advisorOrchard Concha, Marcos
Autordc.contributor.authorTroncoso Kurtovic, Diego Gustavo
Profesor colaboradordc.contributor.otherSilva Sánchez, Jorge
Profesor colaboradordc.contributor.otherMuñoz Carpintero, Diego
Fecha ingresodc.date.accessioned2023-04-26T22:41:42Z
Fecha disponibledc.date.available2023-04-26T22:41:42Z
Fecha de publicacióndc.date.issued2023
Identificadordc.identifier.other10.58011/hp0g-k141
Identificadordc.identifier.urihttps://repositorio.uchile.cl/handle/2250/193059
Resumendc.description.abstractElectric Vehicles (EVs) have gained popularity over the past few years, given their potential to reduce the emission of Greenhouse Gases, which contribute to Climate Change. Researchers have put substantial effort into handling the limitations EVs have, for instance, battery capacity, battery management and charging infrastructure in urban areas. The Electric Vehicle Routing Problem (EVRP) represents a relevant challenge since it directly affects the supply chain of many businesses and could help transitioning to electromobility. Several methods have been used to solve the EVRP incorporating realistic elements, for example, energy consumption models, dynamic and stochastic conditions, charging stations, and others.\\ In this thesis, we designed, implemented and tested a Monte Carlo Tree Search-based algorithm to solve the EVRP on-line. This is an incipient methodology to solve the EVRP and has great potential given its Tree structure, real-time functioning, evaluation of future scenarios and search procedure. Our work shows how the proposed methodology successfully solves the problem online and can update the route given new information. Future efforts aim to add new elements for the EVRP, study the scope of this methodology in other areas where uncertain prognostic information is valuable, explore different strategies in the EVs fleet case, and others.es_ES
Patrocinadordc.description.sponsorshipFONDECYT Chile Grant Nr. 1210031, and the Advanced Center for Electrical and Electronic Engineering, AC3E, Basal Project FB0008, ANIDes_ES
Idiomadc.language.isoenes_ES
Publicadordc.publisherUniversidad de Chilees_ES
Tipo de licenciadc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
Link a Licenciadc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
Palabras clavesdc.subjectAutomóviles eléctricos
Palabras clavesdc.subjectConsumo de energía
Palabras clavesdc.subjectVehículos eléctricos
Palabras clavesdc.subjectMétodo de Monte Carlo
Palabras clavesdc.subjectAprendizaje de máquina
Títulodc.titleA prognostic decision-making approach under uncertainty for an electric vehicle fleet routing problemes_ES
Tipo de documentodc.typeTesises_ES
dc.description.versiondc.description.versionVersión original del autores_ES
dcterms.accessRightsdcterms.accessRightsAcceso abiertoes_ES
Catalogadoruchile.catalogadorgmmes_ES
Departamentouchile.departamentoDepartamento de Ingeniería Eléctricaes_ES
Facultaduchile.facultadFacultad de Ciencias Físicas y Matemáticases_ES
uchile.titulacionuchile.titulacionDoble Titulaciónes_ES
uchile.carrerauchile.carreraIngeniería Civil Eléctricaes_ES
uchile.gradoacademicouchile.gradoacademicoMagisteres_ES
uchile.notadetesisuchile.notadetesisTesis para optar al grado de Magíster en Ciencias de la Ingeniería, Mención Eléctricaes_ES
uchile.notadetesisuchile.notadetesisMemoria para optar al título de Ingeniero Civil Eléctrico


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Attribution-NonCommercial-NoDerivs 3.0 United States
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivs 3.0 United States