Details

Autor(en) / Beteiligte

• Kammammettu, Sanjula
• Yang, Shu-Bo
• Li, Zukui

Titel

Distributionally robust chance-constrained optimization with Gaussian mixture ambiguity set

Ist Teil von

• Computers & chemical engineering, 2024-07, Vol.186, p.108703, Article 108703

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Conventional chance-constrained programming methods suffer from the inexactness of the estimated probability distribution of the underlying uncertainty from data. To this end, a distributionally robust approach to the problem allows for a level of ambiguity considered around a reference distribution. In this work, we propose a novel formulation for the distributionally robust chance-constrained programming problem using an ambiguity set constructed from a variant of optimal transport distance that was developed for Gaussian Mixture Models. We show that for multimodal process uncertainty, our proposed method provides an effective way to incorporate statistical moment information into the ambiguity set construction step, thus leading to improved optimal solutions. We illustrate the performance of our method on a numerical example as well as a chemical process case study. We show that our proposed methodology leverages the multimodal characteristics from the uncertainty data to give superior performance over the traditional Wasserstein distance-based method. •Ambiguity set constructed from optimal transport between Gaussian Mixture Models.•Hedging against the right family of candidate distributions to avoid unnecessary conservatism.•A tractable distributionally robust chance constrained optimization formulation.•Applicability to more generate type of uncertain constraints.•Better objective-constraint satisfaction trade-off performance than classical Wasserstein DRCCP model.