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- W3183234787 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." @default.
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- W3183234787 date "2021-09-01" @default.
- W3183234787 modified "2023-09-26" @default.
- W3183234787 title "Decomposition methods for Wasserstein-based data-driven distributionally robust problems" @default.
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- W3183234787 doi "https://doi.org/10.1016/j.orl.2021.07.007" @default.
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