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Solving Wasserstein Distributionally Robust Combinatorial Optimization Problems

In: Operations Research Proceedings 2023

Author

Listed:
  • Marcel Jackiewicz

    (Wrocław University of Science and Technology)

  • Adam Kasperski

    (Wrocław University of Science and Technology)

  • Paweł Zieliński

    (Wrocław University of Science and Technology)

Abstract

In this paper, a class of combinatorial optimization problems with uncertain objective function costs is considered. The unknown probability distribution for the uncertain cost vector is approximated by an empirical distribution based on an available sample of the cost realizations. The true probability distribution is assumed to lie in a Wasserstein ball centered in the empirical distribution. A solution minimizing the Conditional Value at Risk for a worst probability distribution in the Wasserstein ball is computed. A general method for computing this solution is proposed.

Suggested Citation

  • Marcel Jackiewicz & Adam Kasperski & Paweł Zieliński, 2025. "Solving Wasserstein Distributionally Robust Combinatorial Optimization Problems," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 115-121, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_15
    DOI: 10.1007/978-3-031-58405-3_15
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