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articles: Consumer choice and optimal locations models: Formulations and heuristics


  • Daniel Serra

    () (Dept. of Economics and Business, Universitat Pompeu Fabra, Trias Fargas, 25-27, Barcelona 08005, Spain)

  • Rosa Colomé

    () (Dept. of Economics and Business, Universitat Pompeu Fabra, Trias Fargas, 25-27, Barcelona 08005, Spain)


A new direction of research in competitive location theory incorporates theories of consumer choice behavior in its models. Following this direction, the present article studies the importance of consumer behavior with respect to distance or transportation costs in the optimality of locations obtained by traditional competitive location models. We consider various ways of defining a key parameter in the basic maximum capture model (MAXCAP). This parameter will indicate a number of ways to take distance into account based on several consumer choice behavior theories. The optimal locations and the deviation in demand - captured when the optimal locations of the other models are used instead of the true ones - are computed for each model. We present a metaheuristic based on GRASP and the tabu search procedure to solve all the models. Computational experience and an application to a 55-node network are also presented.

Suggested Citation

  • Daniel Serra & Rosa Colomé, 2001. "articles: Consumer choice and optimal locations models: Formulations and heuristics," Papers in Regional Science, Springer;Regional Science Association International, vol. 80(4), pages 439-464.
  • Handle: RePEc:spr:presci:v:80:y:2001:i:4:p:439-464
    Note: Received: 12 June 1998

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    Cited by:

    1. repec:spr:annopr:v:246:y:2016:i:1:d:10.1007_s10479-014-1676-5 is not listed on IDEAS
    2. Gunhak Lee & Morton E. O'Kelly, 2009. "Exploring Locational Equilibria In A Competitive Broadband Access Market: Theoretical Modeling Approach," Journal of Regional Science, Wiley Blackwell, vol. 49(5), pages 953-975.
    3. Rafael Suárez-Vega & Dolores Santos-Peñate & Pablo Dorta-González, 2014. "Location and quality selection for new facilities on a network market," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 537-560, March.
    4. Haase, Knut & Müller, Sven, 2013. "Management of school locations allowing for free school choice," Omega, Elsevier, vol. 41(5), pages 847-855.
    5. Rafael Suárez-Vega & Dolores R. Santos-Peñate & Pablo Dorta-González, 2004. "Competitive Multifacility Location on Networks: the ("r"∣"X"," Journal of Regional Science, Wiley Blackwell, vol. 44(3), pages 569-588.
    6. Giuseppe Bruno & Andrea Genovese, 2012. "A Spatial Interaction Model for the Representation of the Mobility of University Students on the Italian Territory," Networks and Spatial Economics, Springer, vol. 12(1), pages 41-57, March.
    7. Pelegrín, Blas & Fernández, Pascual & García Pérez, María Dolores, 2015. "On tie breaking in competitive location under binary customer behavior," Omega, Elsevier, vol. 52(C), pages 156-167.

    More about this item


    Distance; competitive location models; consumer choice behavior; GRASP; tabu;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R53 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Public Facility Location Analysis; Public Investment and Capital Stock


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