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dc.contributor.authorHansen, Pierreen
dc.contributor.authorMladenović, Nenaden
dc.date.accessioned2020-05-02T16:42:13Z-
dc.date.available2020-05-02T16:42:13Z-
dc.date.issued2005-12-01en
dc.identifier.isbn978-0-387-23460-8en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2539-
dc.description.abstractVariable Neighborhood Search (VNS) is a recent metaheuristic, or framework for building heuristics, which exploits systematically the idea of neighborhood change, both in the descent to local minima and in the escape from the valleys which contain them. In this tutorial we first present the ingredients of VNS, i.e. Variable Neighborhood Descent (VND) and Reduced VNS (RVNS) followed by the basic and then the general scheme of VNS itself which contain both of them. Extensions are presented, in particular Skewed VNS (SVNS) which enhances exploration of far-away valleys and Variable Neighborhood Decomposition Search (VNDS), a two-level scheme for solution of large instances of various problems. In each case, we present the scheme, some illustrative examples and questions to be addressed in order to obtain an efficient implementation.en
dc.publisherSpringer Link-
dc.relation.ispartofSearch Methodologies: Introductory Tutorials in Optimization and Decision Support Techniquesen
dc.titleVariable neighborhood searchen
dc.typeBook Chapteren
dc.identifier.doi10.1007/0-387-28356-0_8en
dc.identifier.scopus2-s2.0-84891995513en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage211en
dc.relation.lastpage238en
item.cerifentitytypePublications-
item.openairetypeBook Chapter-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0001-6655-0409-
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