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

A 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 ca...

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Vydáno v:	Computers & operations research Ročník 39; číslo 9; s. 2232 - 2241
Hlavní autoři:	Hernández, Patricio, Alonso-Ayuso, Antonio, Bravo, Fernanda, Escudero, Laureano F., Guignard, Monique, Marianov, Vladimir, Weintraub, Andrés
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.09.2012 Elsevier Pergamon Press Inc
Témata:	Algorithms Applied sciences Branch & bound algorithms Branch-and-fix coordination Costs Demand Exact sciences and technology Location analysis Logistics Marketing Mathematical models Mathematical programming Multi-period scenario tree Operational research and scientific management Operational research. Management science Position (location) Prison facility location Prisons Stochastic integer programming Stochastic models Studies Uncertainty Chile Prison facility location Branch-and-fix coordination Stochastic integer programming Multi-period scenario tree Stochastic model Algorithmics Modeling Location problem Optimization Facility location Uncertain system Zero one programming Branching Lower bound Script Probabilistic approach Coordination Branch and bound method Criminality Average cost Cluster Scheduling Scale model Stochastic programming Integer programming Scaling law Upper bound Heuristic method Large scale
ISSN:	0305-0548, 1873-765X, 0305-0548
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Shrnutí:	A 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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0305-0548 1873-765X 0305-0548
DOI:	10.1016/j.cor.2011.11.006