A biased-randomized metaheuristic for the capacitated location routing problem

The location routing problem (LRP) involves the three key decision levels in supply chain design, that is, strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities (e.g., depots or warehouses), (b) assigning customers to facilities, and (c) def...

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Detalles Bibliográficos
Autores: Quintero-Araujo, C., Caballero-Villalobos, J. P., Juan, A., Montoya-Torres, J.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:Colombia
Institución:Universidad de los Andes
Repositorio:Séneca: repositorio Uniandes
Idioma:inglés
OAI Identifier:oai:repositorio.uniandes.edu.co:1992/46967
Acceso en línea:http://hdl.handle.net/1992/46967
Access Level:acceso abierto
Palabra clave:Biased-randomized
Metaheuristic
Capacitated location
Routing problem
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spelling A biased-randomized metaheuristic for the capacitated location routing problemQuintero-Araujo, C.Caballero-Villalobos, J. P.Juan, A.Montoya-Torres, J.Biased-randomizedMetaheuristicCapacitated locationRouting problemThe location routing problem (LRP) involves the three key decision levels in supply chain design, that is, strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities (e.g., depots or warehouses), (b) assigning customers to facilities, and (c) defining routes of vehicles departing from and finishing at each facility to serve the associated customers¿ demands. In this paper, a two¿phase metaheuristic procedure is proposed to deal with the capacitated version of the LRP (CLRP). Here, decisions must be made taking into account limited capacities of both facilities and vehicles. In the first phase (selection of promising solutions), we determine the depots to be opened, perform a fast allocation of customers to open depots, and generate a complete CLRP solution using a fast routing heuristic. This phase is executed several times in order to keep the most promising solutions. In the second phase (solution refinement), for each of the selected solutions we apply a perturbation procedure to the customer allocation followed by a more intensive routing heuristic. Computational experiments are carried out using well¿known instances from the literature. Results show that our approach is quite competitive since it offers average gaps below 0.4% with respect to the best¿known solutions (BKSs) for all tested sets in short computational times.Facultad de Administración2020-10-01T16:50:49Z2020-10-01T16:50:49Z2016Artículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_ab4af688f83e57aaTexthttp://purl.org/redcol/resource_type/ARTp. 1079-1098application/pdfhttp://hdl.handle.net/1992/4696710.1111/itor.12322instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/reponame:Séneca: repositorio Uniandesinstname:Universidad de los Andesinstacron:Universidad de los AndesengAl consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf22022-06-02T14:03:18Z
dc.title.none.fl_str_mv A biased-randomized metaheuristic for the capacitated location routing problem
title A biased-randomized metaheuristic for the capacitated location routing problem
spellingShingle A biased-randomized metaheuristic for the capacitated location routing problem
Quintero-Araujo, C.
Biased-randomized
Metaheuristic
Capacitated location
Routing problem
title_short A biased-randomized metaheuristic for the capacitated location routing problem
title_full A biased-randomized metaheuristic for the capacitated location routing problem
title_fullStr A biased-randomized metaheuristic for the capacitated location routing problem
title_full_unstemmed A biased-randomized metaheuristic for the capacitated location routing problem
title_sort A biased-randomized metaheuristic for the capacitated location routing problem
dc.creator.none.fl_str_mv Quintero-Araujo, C.
Caballero-Villalobos, J. P.
Juan, A.
Montoya-Torres, J.
author Quintero-Araujo, C.
author_facet Quintero-Araujo, C.
Caballero-Villalobos, J. P.
Juan, A.
Montoya-Torres, J.
author_role author
author2 Caballero-Villalobos, J. P.
Juan, A.
Montoya-Torres, J.
author2_role author
author
author
dc.subject.none.fl_str_mv Biased-randomized
Metaheuristic
Capacitated location
Routing problem
topic Biased-randomized
Metaheuristic
Capacitated location
Routing problem
description The location routing problem (LRP) involves the three key decision levels in supply chain design, that is, strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities (e.g., depots or warehouses), (b) assigning customers to facilities, and (c) defining routes of vehicles departing from and finishing at each facility to serve the associated customers¿ demands. In this paper, a two¿phase metaheuristic procedure is proposed to deal with the capacitated version of the LRP (CLRP). Here, decisions must be made taking into account limited capacities of both facilities and vehicles. In the first phase (selection of promising solutions), we determine the depots to be opened, perform a fast allocation of customers to open depots, and generate a complete CLRP solution using a fast routing heuristic. This phase is executed several times in order to keep the most promising solutions. In the second phase (solution refinement), for each of the selected solutions we apply a perturbation procedure to the customer allocation followed by a more intensive routing heuristic. Computational experiments are carried out using well¿known instances from the literature. Results show that our approach is quite competitive since it offers average gaps below 0.4% with respect to the best¿known solutions (BKSs) for all tested sets in short computational times.
publishDate 2016
dc.date.none.fl_str_mv 2016
2020-10-01T16:50:49Z
2020-10-01T16:50:49Z
dc.type.none.fl_str_mv Artículo de revista
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/version/c_ab4af688f83e57aa
Text
http://purl.org/redcol/resource_type/ART
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/1992/46967
10.1111/itor.12322
instname:Universidad de los Andes
reponame:Repositorio Institucional Séneca
repourl:https://repositorio.uniandes.edu.co/
url http://hdl.handle.net/1992/46967
identifier_str_mv 10.1111/itor.12322
instname:Universidad de los Andes
reponame:Repositorio Institucional Séneca
repourl:https://repositorio.uniandes.edu.co/
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv p. 1079-1098
application/pdf
dc.publisher.none.fl_str_mv Facultad de Administración
publisher.none.fl_str_mv Facultad de Administración
dc.source.none.fl_str_mv reponame:Séneca: repositorio Uniandes
instname:Universidad de los Andes
instacron:Universidad de los Andes
instname_str Universidad de los Andes
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institution Universidad de los Andes
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