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A Reliability-Constrained Siting Model with Local Estimates of Busy Fractions

Author

Listed:
  • C ReVelle

    (Department of Geography, Johns Hopkins University, Baltimore, MD 21218, USA)

  • K Hogan

    (Interstate Commission on the Potomac, 6110 Executive Boulevard, Rockville, MD 20852-3903, USA)

Abstract

The concept of location coverage was first applied in the location set covering problem and then extended to the maximal covering location problem. Both of these problems were set in a deterministic framework in which the presence of a service site within the time or distance standard (geographic coverage) was taken as sufficient, even though the possibility existed of the only server within the standard being busy. To correct for this deficiency, Daskin structured the maximal expected covering model and Hogan and ReVelle created a model which maximized backup coverage. A model has now been created which deals explicitly in a chance-constrained sense with the availability of service. Further, this model uses local estimates of busy fractions in the zone around each demand area—in order to reflect more accurately the heterogeneity in service availability.

Suggested Citation

  • C ReVelle & K Hogan, 1988. "A Reliability-Constrained Siting Model with Local Estimates of Busy Fractions," Environment and Planning B, , vol. 15(2), pages 143-152, June.
    • Handle: RePEc:sae:envirb:v:15:y:1988:i:2:p:143-152
    DOI: 10.1068/b150143
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    References listed on IDEAS

    as
    1. Mark S. Daskin, 1983. "A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution," Transportation Science, INFORMS, vol. 17(1), pages 48-70, February.
    2. Mark S. Daskin & Edmund H. Stern, 1981. "A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment," Transportation Science, INFORMS, vol. 15(2), pages 137-152, May.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. 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.
    2. Robert G. Haight & Charles S. Revelle & Stephanie A. Snyder, 2000. "An Integer Optimization Approach to a Probabilistic Reserve Site Selection Problem," Operations Research, INFORMS, vol. 48(5), pages 697-708, October.
    3. 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.
    4. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    5. 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.
    6. Marianov, Vladimir & ReVelle, Charles, 1996. "The Queueing Maximal availability location problem: A model for the siting of emergency vehicles," European Journal of Operational Research, Elsevier, vol. 93(1), pages 110-120, August.

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