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Route optimization for urban last-mile delivery: truck vs. drone performance

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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Engenharia do Ambiente
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorSilva, Adriano S.
dc.contributor.authorBerger, Guido S.
dc.contributor.authorMendes, João
dc.contributor.authorBrito, Thadeu
dc.contributor.authorLima, José
dc.contributor.authorGomes, Helder T.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2026-03-18T14:13:50Z
dc.date.available2026-03-18T14:13:50Z
dc.date.issued2024
dc.description.abstractIn urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. This study compares the effectiveness of delivery using trucks versus drones. Two customer datasets, one clustered and one random, were used for testing. Route optimization involved four deterministic and four non-deterministic algorithms. The performance of these algorithms, considering the total distance traveled, was evaluated across different datasets and vehicle types. The top two algorithms were further assessed for environmental impact and cost efficiency. Battery consumption along the routes was also analyzed to gauge operational feasibility.eng
dc.description.sponsorshipThis work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/057 57/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020); CIMO, UIDB/00690/2020 (DOI: 10.54499/UIDB/00690/2020) and UIDP/00690 /2020 (DOI: 10.54499/UIDP/00690/2020); SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020), Adriano Silva was supported by Doctoral Grant SFRH/BD/151346/2021 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from NORTE 2020, under MIT Portugal Program. Thadeu Brito was supported by FCT PhD Grant Reference SFRH/BD/08598/2020.
dc.identifier.citationSilva, Adriano S.; Berger, Guido S.; Mendes, João; Brito, Thadeu; Lima, José; Gomes, Helder T.; Pereira, Ana I. (2024). Route optimization for urban last-mile delivery: truck vs. drone performance. In 4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024. Cham: Springer Nature. 1, p. 284-299. ISBN 978-303177425-6. DOI: 10.1007/978-3-031-77426-3_19
dc.identifier.doi10.1007/978-3-031-77426-3_19
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10198/36127
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationMountain Research Center
dc.relationMountain Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relationOptimization of municipal solid waste management systems towards sustainability
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectVehicle routing problem
dc.subjectUAVs
dc.subjectGuided local search
dc.titleRoute optimization for urban last-mile delivery: truck vs. drone performanceeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberUIDP/05757/2020
oaire.awardNumberUIDB/00690/2020
oaire.awardNumberUIDP/00690/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardNumberSFRH/BD/151346/2021
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleMountain Research Center
oaire.awardTitleMountain Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardTitleOptimization of municipal solid waste management systems towards sustainability
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oaire.citation.endPage299
oaire.citation.startPage284
oaire.citation.title4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024
oaire.citation.volume1
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