Publication:
Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation

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Date

2021

Authors

Carmo Martins, Leandro do
Tordecilla, Rafael D.
Castaneda, Juliana
Juan, Ángel A.

Director

Publisher

MDPI
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

European Commission/Horizon 2020 Framework Programme/731884openaire
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111100RB-C22/ES/

Abstract

The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.

Keywords

Electric batteries, Vehicle routing problem, Arc routing problem, Team orienteering problem

Department

Estatistika, Informatika eta Matematika / Institute of Smart Cities - ISC / Estadística, Informática y Matemáticas

Faculty/School

Degree

Doctorate program

Editor version

Funding entities

This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.

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