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TorrentFontbona, Ferran
MuÃ±oz SolÃ , VÃctor LÃ³pez IbÃ¡Ã±ez, Beatriz 

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 nonclustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable ComunicaciÃ³ presentada a la â€™Second International Conference on Applied and Theoretical Information Systems Research (2ndATISR2012)â€™ celebrada a Taipei (Taiwan), els dies 27, 28 i 29 de desembre de 2012 

http://hdl.handle.net/2072/205924  
eng  
AttributionNonCommercialNoDerivs 3.0 Spain  
http://creativecommons.org/licenses/byncnd/3.0/es/  
Algorismes  Congressos
Algorithms  Congresses OptimitzaciÃ³ matemÃ tica  Congressos Mathematical optimization  Congresses SoluciÃ³ de problemes  Congressos Problem solving  Congresses SimulaciÃ³, MÃ¨todes de  Congressos Simulation methods  Congresses 

Solving Large LocationAllocation problems by Clustering and Simulated Annealing  
info:eurepo/semantics/conferenceObject  
Recercat 