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Optimization Problems in Graphs with Locational Uncertainty

Author

Listed:
  • Marin Bougeret

    (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier, Centre National de la Recherche Scientifique, 34095 Montpellier, France)

  • Jérémy Omer

    (Institut de Recherche Mathématique de Rennes, Institut National des Sciences Appliquées, 35700 Rennes, France)

  • Michael Poss

    (Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier, Centre National de la Recherche Scientifique, 34095 Montpellier, France)

Abstract

Many discrete optimization problems amount to selecting a feasible set of edges of least weight. We consider in this paper the context of spatial graphs where the positions of the vertices are uncertain and belong to known uncertainty sets. The objective is to minimize the sum of the distances of the chosen set of edges for the worst positions of the vertices in their uncertainty sets. We first prove that these problems are N P -hard even when the feasible sets consist either of all spanning trees or of all s – t paths. Given this hardness, we propose an exact solution algorithm combining integer programming formulations with a cutting plane algorithm, identifying the cases where the separation problem can be solved efficiently. We also propose a conservative approximation and show its equivalence to the affine decision rule approximation in the context of Euclidean distances. We compare our algorithms to three deterministic reformulations on instances inspired by the scientific literature for the Steiner tree problem and a facility location problem.

Suggested Citation

  • Marin Bougeret & Jérémy Omer & Michael Poss, 2023. "Optimization Problems in Graphs with Locational Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 578-592, May.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:3:p:578-592
    DOI: 10.1287/ijoc.2023.1276
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    References listed on IDEAS

    as
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    5. Josette Ayoub & Michael Poss, 2016. "Decomposition for adjustable robust linear optimization subject to uncertainty polytope," Computational Management Science, Springer, vol. 13(2), pages 219-239, April.
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