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A new model for maximal coverage exploiting GIS capabilities

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  • Alexandris, George
  • Giannikos, Ioannis

Abstract

The representation of demand is a key issue which can significantly affect results in several demand covering models. In this paper we concentrate on the well known Maximal Coverage Location Problem and demonstrate that alternative representations of the demand space may lead to largely fluctuating as well as misleading results which seriously overestimate the real coverage achieved by a specified number of servers. We introduce a new model based on the notion of complementary partial coverage and exploit the capabilities of Geographic Information Systems in order to better represent demand. Results of an empirical study indicate that the proposed model is less susceptible to fluctuations for alternative representations of the demand space and that it provides coverage of a larger proportion of demand than traditional models.

Suggested Citation

  • Alexandris, George & Giannikos, Ioannis, 2010. "A new model for maximal coverage exploiting GIS capabilities," European Journal of Operational Research, Elsevier, vol. 202(2), pages 328-338, April.
  • Handle: RePEc:eee:ejores:v:202:y:2010:i:2:p:328-338
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    References listed on IDEAS

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    1. Marianov, Vladimir & Serra, Daniel, 2001. "Hierarchical location-allocation models for congested systems," European Journal of Operational Research, Elsevier, vol. 135(1), pages 195-208, November.
    2. Berman, Oded & Krass, Dmitry & Drezner, Zvi, 2003. "The gradual covering decay location problem on a network," European Journal of Operational Research, Elsevier, vol. 151(3), pages 474-480, December.
    3. George C. Moore & Charles ReVelle, 1982. "The Hierarchical Service Location Problem," Management Science, INFORMS, vol. 28(7), pages 775-780, July.
    4. Galvao, Roberto Dieguez & ReVelle, Charles, 1996. "A Lagrangean heuristic for the maximal covering location problem," European Journal of Operational Research, Elsevier, vol. 88(1), pages 114-123, January.
    5. Mcknew, Mark A. & Saydam, Cem & Fitzpatrick, Kathy E., 1989. "A simple model for predicting the number of servers necessary to cover a region," Socio-Economic Planning Sciences, Elsevier, vol. 23(6), pages 345-353.
    6. Panagiotis Mitropoulos & Ioannis Mitropoulos & Ioannis Giannikos & Aris Sissouras, 2006. "A biobjective model for the locational planning of hospitals and health centers," Health Care Management Science, Springer, vol. 9(2), pages 171-179, May.
    7. R. Francis & T. Lowe & M. Rayco & A. Tamir, 2009. "Aggregation error for location models: survey and analysis," Annals of Operations Research, Springer, vol. 167(1), pages 171-208, March.
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    Cited by:

    1. repec:wsi:ijitdm:v:16:y:2017:i:01:n:s0219622016500462 is not listed on IDEAS
    2. Murray, Alan T. & Wei, Ran, 2013. "A computational approach for eliminating error in the solution of the location set covering problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 52-64.
    3. Murray, Alan T. & Feng, Xin, 2016. "Public street lighting service standard assessment and achievement," Socio-Economic Planning Sciences, Elsevier, vol. 53(C), pages 14-22.
    4. Baray, Jérôme & Cliquet, Gérard, 2013. "Optimizing locations through a maximum covering/p-median hierarchical model: Maternity hospitals in France," Journal of Business Research, Elsevier, vol. 66(1), pages 127-132.
    5. Ran Wei & Alan Murray & Rajan Batta, 2014. "A bounding-based solution approach for the continuous arc covering problem," Journal of Geographical Systems, Springer, vol. 16(2), pages 161-182, April.
    6. Caro Vela, María Dolores & Paralera Morales, Concepción, 2011. "Una propuesta para la localización de áreas de servicio y descanso adaptadas al transporte de mercancías peligrosas mediante un modelo de optimización; aplicación al territorio español = A Proposal fo," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 11(1), pages 17-32, June.

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