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Greedy Randomized Adaptive Search for a Location Problem with Economies of Scale

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Developments in Global Optimization

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 18))

Abstract

We consider a heuristic approach for the solution of a location problem with economies of scale. The method chosen has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors. We define the various components comprising this GRASP approach and perform a step-by-step development of such a heuristic for the location problem with concave costs. Computational results for problems of dimensions up to 100 × 1000 are reported.

Research partially supported by CENIIT (Center for Industrial Information Technology)

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Author information

Authors and Affiliations

  1. Division of Optimization, Department of Mathematics, Linköping Institute of Technology, S-581 83, Linköping, Sweden

    K. Holmqvist & A. Migdalas

  2. Center for Applied Optimization and Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611-6595, USA

    P. M. Pardalos

Authors
  1. K. Holmqvist
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  2. A. Migdalas
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  3. P. M. Pardalos
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Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Immanuel M. Bomze

  2. József Attila University, Szeged, Hungary

    Tibor Csendes

  3. University of Trier, Trier, Germany

    Reiner Horst

  4. University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

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© 1997 Springer Science+Business Media Dordrecht

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Holmqvist, K., Migdalas, A., Pardalos, P.M. (1997). Greedy Randomized Adaptive Search for a Location Problem with Economies of Scale. In: Bomze, I.M., Csendes, T., Horst, R., Pardalos, P.M. (eds) Developments in Global Optimization. Nonconvex Optimization and Its Applications, vol 18. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2600-8_18

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  • DOI: https://doi.org/10.1007/978-1-4757-2600-8_18

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4768-0

  • Online ISBN: 978-1-4757-2600-8

  • eBook Packages: Springer Book Archive

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