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Location Science pp 177–203Cite as

Facility Location Under Uncertainty

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Abstract

In this chapter, we cover some essential knowledge on facility location under uncertainty. We put a major emphasis on modeling aspects related with discrete facility location problems. Different modeling frameworks are discussed. In particular, we distinguish between robust optimization, stochastic programming and chance-constrained models. We also discuss relevant aspects such as solution techniques, multi-stage stochastic programming models, scenario generation, and extensions of basic problems.

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Notes

  1. 1.

    In each scenario, the regret of a solution is the difference between the cost of the solution if the scenario occurs and the optimal cost that can be achieved under that scenario (see Kouvelis and Yu 1997 for further details).

  2. 2.

    A decision should depend only on the information available at the time it is made (see Rockafellar and Wets 1991).

  3. 3.

    Measure of how much the return on investment is below a target initially imposed.

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Author information

Authors and Affiliations

  1. Departamento de Matemática, Centro de Matemática e Aplicações, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Isabel Correia

  2. Departamento de Estatística e Investigação Operacional, Centro de Investigação Operacional, Universidade de Lisboa Campo Grande, 1749-016, Lisboa, Portugal

    Francisco Saldanha da Gama

Authors
  1. Isabel Correia
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  2. Francisco Saldanha da Gama
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Corresponding author

Correspondence to Isabel Correia .

Editor information

Editors and Affiliations

  1. HEC Montréal, Montréal, Canada

    Gilbert Laporte

  2. Institute of Operations Research (IOR), Karlsruhe Institute of Technology, Karlsruhe, Germany

    Stefan Nickel

  3. DEIO-FCUL, University of Lisbon, Lisbon, Portugal

    Francisco Saldanha da Gama

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Correia, I., da Gama, F.S. (2015). Facility Location Under Uncertainty. In: Laporte, G., Nickel, S., Saldanha da Gama, F. (eds) Location Science. Springer, Cham. https://doi.org/10.1007/978-3-319-13111-5_8

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