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The Ex-Post Evaluation of the Minimum Local Reliability Level: An Enhanced Probabilistic Location Set Covering Model

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  • Fernando Borrás
  • Jesús Pastor

Abstract

In order to evaluate the behavior of three probabilistic location set covering models we propose a new method that allows the ex-post measurement of the so called “minimum local reliability level”, both under the independence and under the dependence assumption. We show experimentally, by means of a set of test problems, that the proposed loss-system version of Ball and Lin's model (1993) does almost always achieve the required reliability level. Moreover, if we compare this new version with the other two already known probabilistic models and consider as a second additional criteria the least number of required vehicles, we show that the new version has a better behavior both under the independence and the dependence assumption. In this paper we further propose a new model with the aim of reducing the number of required vehicles while satisfying the fixed reliability level. Our new model is formulated like Ball and Lin's model incorporating the servers workload estimate of the Binomial PLSCP of ReVelle and Hogan (1988). Finally, we check the precision of our ex-post evaluation method over the four considered models through a simulation study. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • Fernando Borrás & Jesús Pastor, 2002. "The Ex-Post Evaluation of the Minimum Local Reliability Level: An Enhanced Probabilistic Location Set Covering Model," Annals of Operations Research, Springer, vol. 111(1), pages 51-74, March.
    • Handle: RePEc:spr:annopr:v:111:y:2002:i:1:p:51-74:10.1023/a:1020941400807
    DOI: 10.1023/A:1020941400807
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    Cited by:

    1. Marcos Singer & Patricio Donoso & Natalia Jadue, 2004. "Evaluacion De Las Oportunidades De Mejoramiento De La Logistica Directa De Emergencia," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 7(2), pages 179-209.
    2. Leknes, Håkon & Aartun, Eirik Skorge & Andersson, Henrik & Christiansen, Marielle & Granberg, Tobias Andersson, 2017. "Strategic ambulance location for heterogeneous regions," European Journal of Operational Research, Elsevier, vol. 260(1), pages 122-133.
    3. Sun Hoon Kim & Young Hoon Lee, 2016. "Iterative optimization algorithm with parameter estimation for the ambulance location problem," Health Care Management Science, Springer, vol. 19(4), pages 362-382, December.
    4. Soo-Haeng Cho & Hoon Jang & Taesik Lee & John Turner, 2014. "Simultaneous Location of Trauma Centers and Helicopters for Emergency Medical Service Planning," Operations Research, INFORMS, vol. 62(4), pages 751-771, August.
    5. Beraldi, P. & Bruni, M.E., 2009. "A probabilistic model applied to emergency service vehicle location," European Journal of Operational Research, Elsevier, vol. 196(1), pages 323-331, July.
    6. Sorensen, Paul & Church, Richard, 2010. "Integrating expected coverage and local reliability for emergency medical services location problems," Socio-Economic Planning Sciences, Elsevier, vol. 44(1), pages 8-18, March.
    7. Boffey, Brian & Galvao, Roberto & Espejo, Luis, 2007. "A review of congestion models in the location of facilities with immobile servers," European Journal of Operational Research, Elsevier, vol. 178(3), pages 643-662, May.
    8. Marcos Singer & Patricio Donoso & Alan Scheller-Wolf, 2008. "Una Introducción A La Teoría De Colas Aplicada A La Gestión De Servicios," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 11(2), pages 93-120.
    9. Wang, Wei & Wu, Shining & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2021. "Emergency facility location problems in logistics: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    10. Shariat-Mohaymany, Afshin & Babaei, Mohsen & Moadi, Saeed & Amiripour, Sayyed Mahdi, 2012. "Linear upper-bound unavailability set covering models for locating ambulances: Application to Tehran rural roads," European Journal of Operational Research, Elsevier, vol. 221(1), pages 263-272.

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