Kernel distributionally robust chance-constrained process optimization

Yang, Shu‐Bo; Li, Zukui

doi:10.1016/j.compchemeng.2022.107953

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…As the comparisons to the proposed Sinkhorn DRCCP, the Wasserstein DRCCP and the sample average approximation (SAA)-based method 44 are also applied to the same problem. We use the tractable models in our previous work 45 The above-mentioned results are shown in the following tables.…”

Section: Numerical Examplementioning

confidence: 99%

“…As the comparisons to the proposed Sinkhorn DRCCP, the Wasserstein DRCCP and the sample average approximation (SAA)‐based method 44 are also applied to the same problem. We use the tractable models in our previous work 45 for the Wasserstein DRCCP and the SAA‐based method in this study. The employed models of the Wasserstein DRCCP (based on the type‐1 Wasserstein distance with

ℓ_{2}

norm) and the SAA‐based method are shown in 23 in Appendix S1.…”

Section: Numerical Examplementioning

confidence: 99%

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Distributionally robust chance‐constrained optimization with Sinkhorn ambiguity set

Yang

2023

AIChE Journal

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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.

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Section: Numerical Examplementioning

confidence: 99%