Data-driven robust chance constrained problems: a mixture model approach

DOI10.1007/s10957-018-1376-4zbMath1402.90104OpenAlexW2888717625WikidataQ129398875 ScholiaQ129398875MaRDI QIDQ1626548

Shen Peng, Zhiping Chen, Jia Liu

Publication date: 27 November 2018

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-018-1376-4

zbMATH Keywords

mixture distributiondistributionally robust optimizationdata-drivenconvex approximationchance constrained problem

Mathematics Subject Classification ID

Convex programming (90C25) Stochastic programming (90C15)

Related Items (5)

A novel methodology for portfolio selection in fuzzy multi criteria environment using risk-benefit analysis and fractional stochastic ⋮ Stochastic mathematical programs with probabilistic complementarity constraints: SAA and distributionally robust approaches ⋮ A new data-driven robust optimization approach to multi-item newsboy problems ⋮ A sparse chance constrained portfolio selection model with multiple constraints ⋮ Distributionally Robust Chance Constrained Geometric Optimization

Uses Software

• QRM

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