Data-Driven Robust Chance Constrained Problems: A Mixture Model Approach

作者：Chen, Zhiping^*; Peng, Shen; Liu, Jia

来源：Journal of Optimization Theory and Applications, 2018, 179(3): 1065-1085.

DOI：10.1007/s10957-018-1376-4

• Data-driven
• Mixture distribution
• Distributionally robust optimization
• Chance constrained problem
• Convex approximation

This paper discusses the mixture distribution-based data-driven robust chance constrained problem. We construct a data-driven mixture distribution-based uncertainty set from the perspective of simultaneously estimating higher-order moments. Then, we derive a reformulation of the data-driven robust chance constrained problem. As the reformulation is not a convex programming problem, we propose new and tight convex approximations based on the piecewise linear approximation method. We establish the theoretical foundation for these approximations. Finally, numerical results show that the proposed approximations are practical and efficient.

• 出版日期2018-12
• 单位西安交通大学