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Performance benchmarking of or-tools methods for capacitated vehicle routing problems with time windows

2026Comunicação em conferência Acesso restrito
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Engenharia Mecânica
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorSena, Inês
dc.contributor.authorRibeiro, Tiago B.
dc.contributor.authorSilva, Adriano S.
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorCosta, Lino A.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2025-12-04T16:04:42Z
dc.date.available2025-12-04T16:04:42Z
dc.date.issued2026
dc.description.abstractThe Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a significant challenge in combinatorial optimization, with extensive practical applications in logistics and transportation. This study aims to conduct a comparative analysis of the various methods available in OR-Tools for solving the CVRPTW across datasets of different sizes and types using the Solomon and the Gehring and Homberger benchmarks. The analysis provided insights into the relative strengths of each method, with a primary focus on Guided Local Search (GLS) and Tabu Search (TS), showing consistent performance and adaptability to different dataset characteristics. The results indicate that GLS is the most robust method overall, and TS can outperform it in specific scenarios. In conclusion, this study offers insights for selecting the most effective method to solve vehicle routing problems based on the characteristics and scale of the problem.eng
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/ 05757/2020 (DOI: 10.54499/UIDB/05757/2020) and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). The authors are grateful to Sociedade Ponto Verde for the finantial support through the project “A digitalização como ferramenta para melhorar a sustentabilidade do processo de recolha seletiva”.
dc.identifier.citationSena, Inês; Ribeiro, Tiago B.; Silva, Adriano S.; Fernandes, Florbela P.; Costa, Lino A.; Pereira, Ana I. (2026). Performance benchmarking of or-tools methods for capacitated vehicle routing problems with time windows. In Optimization, Learning Algorithms and Applications: 5th International Conference - Part 1. Cham: Springer Nature. p. 95–110. ISBN 9783032001368
dc.identifier.doi10.1007/978-3-032-00137-5_7
dc.identifier.isbn9783032001368
dc.identifier.isbn9783032001375
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10198/35176
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.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCapacitated Vehicle Routing Problem
dc.subjectOptimization
dc.subjectOR-Tools
dc.subjectTime Windows
dc.titlePerformance benchmarking of or-tools methods for capacitated vehicle routing problems with time windowseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage110
oaire.citation.startPage95
oaire.citation.titleOptimization, Learning Algorithms and Applications: 5th International Conference - Part 1
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSena
person.familyNameSilva
person.familyNameFernandes
person.familyNamePereira
person.givenNameInês
person.givenNameAdriano S.
person.givenNameFlorbela P.
person.givenNameAna I.
person.identifier.ciencia-idDC10-817D-21B5
person.identifier.ciencia-id3E14-5049-B09D
person.identifier.ciencia-id501D-6FD0-CC53
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0003-4995-4799
person.identifier.orcid0000-0002-6795-2335
person.identifier.orcid0000-0001-9542-4460
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id57222722951
person.identifier.scopus-author-id35179471000
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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