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Solving Robust Two-Stage Combinatorial Optimization Problems Under Convex Uncertainty

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Operations Research Proceedings 2019

Part of the book series: Operations Research Proceedings ((ORP))

Abstract

In this paper a class of robust two-stage combinatorial optimization problems with uncertain costs is discussed. It is assumed that the uncertainty is modeled by using a convex uncertainty set, for example of polyhedral or ellipsoidal shape. Several methods to compute exact and approximate solutions are introduced. Experimental results for robust two-stage version of the weighted set cover problem are presented.

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Acknowledgements

Adam Kasperski and Paweł Zieliński were supported by the National Science Centre, Poland, grant 2017/25/B/ST6/00486.

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Authors and Affiliations

  1. Network and Data Science Management, University of Siegen, Siegen, Germany

    Marc Goerigk

  2. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Adam Kasperski

  3. Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, Wrocław, Poland

    Paweł Zieliński

Authors
  1. Marc Goerigk
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  2. Adam Kasperski
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  3. Paweł Zieliński
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Corresponding author

Correspondence to Adam Kasperski .

Editor information

Editors and Affiliations

  1. Faculty of Business and Economics, TU Dresden, Dresden, Germany

    Janis S. Neufeld

  2. Faculty of Business and Economics, TU Dresden, Dresden, Germany

    Udo Buscher

  3. Faculty of Business and Economics, TU Dresden, Dresden, Germany

    Rainer Lasch

  4. Faculty of Business and Economics, TU Dresden, Dresden, Germany

    Dominik Möst

  5. Faculty of Transportation and Traffic, TU Dresden, Dresden, Germany

    Jörn Schönberger

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Goerigk, M., Kasperski, A., Zieliński, P. (2020). Solving Robust Two-Stage Combinatorial Optimization Problems Under Convex Uncertainty. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_51

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