Decomposition methods for Wasserstein-based data-driven distributionally robust problems

Carlos Andrés Gamboa, Davi Michel Valladão, Alexandre Street, Tito Homem-de-Mello

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We study decomposition methods for two-stage data-driven Wasserstein-based DROs with right-hand-sided uncertainty and rectangular support. We propose a novel finite reformulation that explores the rectangular uncertainty support to develop and test five new different decomposition schemes: Column-Constraint Generation, Single-cut and Multi-cut Benders, as well as Regularized Single-cut and Multi-cut Benders. We compare the efficiency of the proposed methods for a unit commitment problem with 14 and 54 thermal generators whose uncertainty vector differs from a 24 to 240-dimensional array.

Original languageEnglish
Pages (from-to)696-702
Number of pages7
JournalOperations Research Letters
Volume49
Issue number5
DOIs
StatePublished - Sep 2021
Externally publishedYes

Keywords

  • Decomposition methods
  • Distributionally robust optimization
  • Two-stage stochastic programming

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