Solving multifacility Huff location models on networks using metaheuristic and exact approaches

In this paper we consider multifacility Huff facility location problem on networks. First, we introduce a slight modification of the existing mixed integer nonlinear mathematical model and confirm its validity by using the solver for nonlinear optimization, KNITRO. Second, since the problem is NP-ha...

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Detalles Bibliográficos
Autores: Grohmann, Sanja, Urošević, Dragan, Carrizosa Priego, Emilio José, Mladenović, Nenad
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/107834
Acceso en línea:https://hdl.handle.net/11441/107834
https://doi.org/10.1016/j.cor.2016.03.005
Access Level:acceso abierto
Palabra clave:Location
Networks
Competitive location
Metaheuristics
Variable Neighborhood Search
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spelling Solving multifacility Huff location models on networks using metaheuristic and exact approachesGrohmann, SanjaUrošević, DraganCarrizosa Priego, Emilio JoséMladenović, NenadLocationNetworksCompetitive locationMetaheuristicsVariable Neighborhood SearchIn this paper we consider multifacility Huff facility location problem on networks. First, we introduce a slight modification of the existing mixed integer nonlinear mathematical model and confirm its validity by using the solver for nonlinear optimization, KNITRO. Second, since the problem is NP-hard, we develop three methods that are based on three metaheuristic principles: Variable Neighborhood Search, Simulated Annealing, and Multi-Start Local Search. Based on extensive computational experiments on large size instances (up to 800 customers and 100 potential facilities), it appears that VNS based heuristic outperforms the other two proposed methods.PERGAMON-ELSEVIER SCIENCE LTDEstadística e Investigación OperativaFQM329: Optimización2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/107834https://doi.org/10.1016/j.cor.2016.03.005reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputers & Operations Research, 78, 537-546.https://doi.org/10.1016/j.cor.2016.03.005info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1078342026-06-17T12:51:07Z
dc.title.none.fl_str_mv Solving multifacility Huff location models on networks using metaheuristic and exact approaches
title Solving multifacility Huff location models on networks using metaheuristic and exact approaches
spellingShingle Solving multifacility Huff location models on networks using metaheuristic and exact approaches
Grohmann, Sanja
Location
Networks
Competitive location
Metaheuristics
Variable Neighborhood Search
title_short Solving multifacility Huff location models on networks using metaheuristic and exact approaches
title_full Solving multifacility Huff location models on networks using metaheuristic and exact approaches
title_fullStr Solving multifacility Huff location models on networks using metaheuristic and exact approaches
title_full_unstemmed Solving multifacility Huff location models on networks using metaheuristic and exact approaches
title_sort Solving multifacility Huff location models on networks using metaheuristic and exact approaches
dc.creator.none.fl_str_mv Grohmann, Sanja
Urošević, Dragan
Carrizosa Priego, Emilio José
Mladenović, Nenad
author Grohmann, Sanja
author_facet Grohmann, Sanja
Urošević, Dragan
Carrizosa Priego, Emilio José
Mladenović, Nenad
author_role author
author2 Urošević, Dragan
Carrizosa Priego, Emilio José
Mladenović, Nenad
author2_role author
author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimización
dc.subject.none.fl_str_mv Location
Networks
Competitive location
Metaheuristics
Variable Neighborhood Search
topic Location
Networks
Competitive location
Metaheuristics
Variable Neighborhood Search
description In this paper we consider multifacility Huff facility location problem on networks. First, we introduce a slight modification of the existing mixed integer nonlinear mathematical model and confirm its validity by using the solver for nonlinear optimization, KNITRO. Second, since the problem is NP-hard, we develop three methods that are based on three metaheuristic principles: Variable Neighborhood Search, Simulated Annealing, and Multi-Start Local Search. Based on extensive computational experiments on large size instances (up to 800 customers and 100 potential facilities), it appears that VNS based heuristic outperforms the other two proposed methods.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/107834
https://doi.org/10.1016/j.cor.2016.03.005
url https://hdl.handle.net/11441/107834
https://doi.org/10.1016/j.cor.2016.03.005
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computers & Operations Research, 78, 537-546.
https://doi.org/10.1016/j.cor.2016.03.005
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv PERGAMON-ELSEVIER SCIENCE LTD
publisher.none.fl_str_mv PERGAMON-ELSEVIER SCIENCE LTD
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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