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Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Comunicació presentada a la ’Second International Conference on Applied and Theoretical Information Systems Research (2nd-ATISR2012)’ celebrada a Taipei (Taiwan), els dies 27, 28 i 29 de desembre de 2012

Author: Torrent-Fontbona, Ferran
Muñoz Solà, Víctor
López Ibáñez, Beatriz
Date: 2013 January 9
Abstract: Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable
Comunicació presentada a la ’Second International Conference on Applied and Theoretical Information Systems Research (2nd-ATISR2012)’ celebrada a Taipei (Taiwan), els dies 27, 28 i 29 de desembre de 2012
Format: application/pdf
Document access: http://hdl.handle.net/10256/7381
Language: eng
Collection: Contribucions a Congressos (D-EEEiA)
Rights: Attribution-NonCommercial-NoDerivs 3.0 Spain
Rights URI: http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Subject: Algorismes -- Congressos
Algorithms -- Congresses
Optimització matemàtica -- Congressos
Mathematical optimization -- Congresses
Solució de problemes -- Congressos
Problem solving -- Congresses
Simulació, Mètodes de -- Congressos
Simulation methods -- Congresses
Title: Solving Large Location-Allocation problems by Clustering and Simulated Annealing
Type: info:eu-repo/semantics/conferenceObject
Repository: DUGiDocs

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