DC FieldValueLanguage
dc.contributor.authorBrimberg, Jacken
dc.contributor.authorMladenović, Nenaden
dc.contributor.authorTodosijević, Racaen
dc.contributor.authorUrošević, Draganen
dc.date.accessioned2020-05-01T20:13:52Z-
dc.date.available2020-05-01T20:13:52Z-
dc.date.issued2019-01-01en
dc.identifier.issn0254-5330en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/1763-
dc.description.abstractVariable neighborhood search (VNS) is a proven heuristic framework for finding good solutions to combinatorial and global optimization problems. In this paper two VNS-based heuristics are proposed for solving the capacitated clustering problem. The first follows a standard VNS approach, and the second a skewed VNS that allows moves to inferior solutions. The performance of the two heuristics is assessed on benchmark instances from the literature. We also compare their performance against a recently published iterated VNS procedure. All VNS procedures outperform the state-of-the-art, but the Skewed VNS is best overall. This would suggest that using acceptance criteria before allowing moves to inferior solutions in Skewed VNS is preferable to the random shaking approach that is used in Iterated VNS to move to new regions of the solution space.en
dc.publisherSpringer Link-
dc.relationMathematical Modelas and Optimization Methods on Large-Scale Systems-
dc.relationDevelopment of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education-
dc.relation.ispartofAnnals of Operations Researchen
dc.subjectClustering | Heuristic | Local search | Optimizationen
dc.titleSolving the capacitated clustering problem with variable neighborhood searchen
dc.typeArticleen
dc.identifier.doi10.1007/s10479-017-2601-5en
dc.identifier.scopus2-s2.0-85027012339en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage289en
dc.relation.lastpage321en
dc.relation.issue1-2en
dc.relation.volume272en
dc.description.rankM22-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
crisitem.author.orcid0000-0001-6655-0409-
crisitem.author.orcid0000-0002-9321-3464-
crisitem.author.orcid0000-0003-3607-6704-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/174010e.php-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/044006e.php-
crisitem.project.fundingProgramDirectorate for Engineering-
crisitem.project.fundingProgramNATIONAL HEART, LUNG, AND BLOOD INSTITUTE-
crisitem.project.openAireinfo:eu-repo/grantAgreement/NSF/Directorate for Engineering/1740103-
crisitem.project.openAireinfo:eu-repo/grantAgreement/NIH/NATIONAL HEART, LUNG, AND BLOOD INSTITUTE/5R01HL044006-04-
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