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Robust optimization: Sensitivity to uncertainty in scalar and vector cases, with applications

Author

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  • Crespi, Giovanni P.
  • Kuroiwa, Daishi
  • Rocca, Matteo

Abstract

The question we address is how robust solutions react to changes in the uncertainty set. We prove the location of robust solutions with respect to the magnitude of a possible decrease in uncertainty, namely when the uncertainty set shrinks, and convergence of the sequence of robust solutions.

Suggested Citation

  • Crespi, Giovanni P. & Kuroiwa, Daishi & Rocca, Matteo, 2018. "Robust optimization: Sensitivity to uncertainty in scalar and vector cases, with applications," Operations Research Perspectives, Elsevier, vol. 5(C), pages 113-119.
    • Handle: RePEc:eee:oprepe:v:5:y:2018:i:c:p:113-119
    DOI: 10.1016/j.orp.2018.03.001
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    References listed on IDEAS

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    Cited by:

    1. Frauke Liers & Lars Schewe & Johannes Thürauf, 2022. "Radius of Robust Feasibility for Mixed-Integer Problems," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 243-261, January.

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