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Huff-Like Stackelberg Location Problems on the Plane

In: Spatial Interaction Models

Author

Listed:
  • José Fernández

    (University of Murcia)

  • Juana L. Redondo

    (University of Almería)

  • Pilar M. Ortigosa

    (University of Almería)

  • Boglárka G.-Tóth

    (University of Szeged)

Abstract

The so-called leader-follower (or Stackelberg) problem is researched. A chain, the leader, wants to locate a single new facility in a region of the plane. After that, as a reaction, the competitor chain, the follower, will locate a single new facility too, knowing the decision taken by the leader. Several variants of the problem are analyzed. In the simplest one, the objective of both the leader and the follower is to maximize the market share, the qualities of the facilities to be located are given beforehand, and the demand is fixed (no costs are considered). In the second one, the qualities of the facilities to be located are considered variables of the problem, and costs related both to location and quality are taken into account; the demand is fixed as in the first model. Finally, the last model extends the previous one considering that the demand varies depending on the location and the quality of the facilities. Exact (for the first problem) and heuristic (for the second and third problems) approaches proposed for the aforementioned location models are described and analyzed. High performance computing approaches for the heuristic methods are also reviewed. A new exact branch-and-bound method for the last two problems is also suggested.

Suggested Citation

  • José Fernández & Juana L. Redondo & Pilar M. Ortigosa & Boglárka G.-Tóth, 2017. "Huff-Like Stackelberg Location Problems on the Plane," Springer Optimization and Its Applications, in: Lina Mallozzi & Egidio D'Amato & Panos M. Pardalos (ed.), Spatial Interaction Models, pages 129-169, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-52654-6_7
    DOI: 10.1007/978-3-319-52654-6_7
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    Cited by:

    1. Valipour, Mohammad & Khoshkam, Helaleh & Bateni, Sayed M. & Jun, Changhyun & Band, Shahab S., 2023. "Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States," Agricultural Water Management, Elsevier, vol. 283(C).

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