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

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

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

8 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)696-702
Número de páginas7
PublicaciónOperations Research Letters
Volumen49
N.º5
DOI
EstadoPublicada - sep. 2021
Publicado de forma externa

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