A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty

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dc.contributor.authorHernandez, Patricio
dc.contributor.authorAlonso Ayuso, Antonio
dc.contributor.authorBravo, Fernanda
dc.contributor.authorEscudero, Laureano F.
dc.contributor.authorGuignard, Monique
dc.contributor.authorMarianov, Vladimir
dc.contributor.authorWeintraub, Andres
dc.date.accessioned2024-01-10T12:37:22Z
dc.date.available2024-01-10T12:37:22Z
dc.date.issued2012
dc.description.abstractA multi-period stochastic model and an algorithmic approach to location of prison facilities under uncertainty are presented and applied to the Chilean prison system. The problem consists of finding locations and sizes of a preset number of new jails and determining where and when to increase the capacity of both new and existing facilities over a time horizon, while minimizing the expected costs of the prison system. Constraints include maximum inmate transfer distances, upper and lower bounds for facility capacities, and scheduling of facility openings and expansion, among others. The uncertainty lies in the future demand for capacity, because of the long time horizon under study and because of the changes in criminal laws, which could strongly modify the historical tendencies of penal population growth. Uncertainty comes from the effects of penal reform in the capacity demand. It is represented in the model through probabilistic scenarios, and the large-scale model is solved via a heuristic mixture of branch-and-fix coordination and branch-and-bound schemes to satisfy the constraints in all scenarios, the so-called branch-and-cluster coordination scheme. We discuss computational experience and compare the results obtained for the minimum expected cost and average scenario strategies. Our results demonstrate that the minimum expected cost solution leads to better solutions than does the average scenario approach. Additionally, the results show that the stochastic algorithmic approach that we propose outperforms the plain use of a state-of-the-art optimization engine, at least for the three versions of the real-life case that have been tested by us. (c) 2011 Published by Elsevier Ltd.
dc.description.funderFondecyt Chile
dc.description.funderMilenium Institute Complex Engineering Systems Chile
dc.description.funderMinistry of Science and Innovation Spain
dc.description.funderComunidad de Madrid Spain
dc.description.funderNational Science Foundation USA
dc.fechaingreso.objetodigital2024-04-02
dc.format.extent10 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.cor.2011.11.006
dc.identifier.eissn1873-765X
dc.identifier.issn0305-0548
dc.identifier.urihttps://doi.org/10.1016/j.cor.2011.11.006
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76828
dc.identifier.wosidWOS:000301216600028
dc.information.autorucIngeniería;Marianov V;S/I;99349
dc.issue.numero9
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final2241
dc.pagina.inicio2232
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.revistaCOMPUTERS & OPERATIONS RESEARCH
dc.rightsacceso restringido
dc.subjectPrison facility location
dc.subjectStochastic integer programming
dc.subjectMulti-period scenario tree
dc.subjectBranch-and-fix coordination
dc.subjectSTOCHASTIC PROGRAMS
dc.subjectDECOMPOSITION
dc.titleA branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty
dc.typeartículo
dc.volumen39
sipa.codpersvinculados99349
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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