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Kernel distributionally robust chance-constrained process optimization
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DOI：10.1016/j.compchemeng.2022.107953.png)
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Abstract
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A kernel distributionally robust chance-constrained optimization (DRCCP) method is proposed in this study based on the kernel ambiguity set. The kernel ambiguity set is established via the kernel mean embedding (KME) and the maximum mean discrepancy (MMD) between distributions. The proposed approach can be formulated as two different models. The first one is a mixed-integer model employing the indicator function for handling the joint chance constraint. The second one is a continuous optimization model using the Conditional Value-at-Risk (CVaR) approximation to approximate the indicator function. The proposed method is compared with the popular Wasserstein ambiguity set based approach. A numerical example and a nonlinear process optimization problem are studied to demonstrate its efficacy.
Keywords:
Kernel ambiguity set
Kernel mean embedding
Maximum mean discrepancy
Distributionally robust chance-constrained programming
Joint chance constraint
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