TY - JOUR
T1 - Decomposition methods for Wasserstein-based data-driven distributionally robust problems
AU - Gamboa, Carlos Andrés
AU - Valladão, Davi Michel
AU - Street, Alexandre
AU - Homem-de-Mello, Tito
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Decomposition methods
KW - Distributionally robust optimization
KW - Two-stage stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=85111692846&partnerID=8YFLogxK
U2 - 10.1016/j.orl.2021.07.007
DO - 10.1016/j.orl.2021.07.007
M3 - Article
AN - SCOPUS:85111692846
SN - 0167-6377
VL - 49
SP - 696
EP - 702
JO - Operations Research Letters
JF - Operations Research Letters
IS - 5
ER -