Distributionally robust chance‐constrained optimization with Sinkhorn ambiguity set

A novel distributionally robust chance‐constrained optimization (DRCCP) method is proposed in this work based on the Sinkhorn ambiguity set. The Sinkhorn ambiguity set is constructed based on the Sinkhorn distance, which is a variant of the Wasserstein distance with the entropic regularization. The...

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Bibliographic Details
Published in:AIChE journal 2023-10, Vol.69 (10), p.n/a
Main Authors: Yang, Shu‐Bo, Li, Zukui
Format: Article
Language:English
Subjects:
Ambiguity
Constraint modelling
data‐driven optimization
distributionally robust chance‐constrained optimization
joint chance constraint
Optimization
Regularization
Robustness (mathematics)
Sinkhorn ambiguity set
Sinkhorn distance
Uncertainty
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Summary:A novel distributionally robust chance‐constrained optimization (DRCCP) method is proposed in this work based on the Sinkhorn ambiguity set. The Sinkhorn ambiguity set is constructed based on the Sinkhorn distance, which is a variant of the Wasserstein distance with the entropic regularization. The proposed method can hedge against more general families of uncertainty distributions than the Wasserstein ambiguity set‐based methods. The presented approach is formulated as a tractable conic model based on the Conditional value‐at‐risk (CVaR) approximation and the discretized kernel distribution relaxation. This model is compatible with more general constraints that are subject to uncertainty than the Wasserstein‐based methods. Accordingly, the presented Sinkhorn DRCCP is a more practical approach that overcomes the limitations of the traditional Wasserstein DRCCP approaches. A numerical example and a nonlinear chemical process optimization case are studied to demonstrate the efficacy of the Sinkhorn DRCCP and its advantages over the Wasserstein DRCCP.
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.18177