Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods

Chao Ning, Fengqi You

Abstract: Highlights•A novel data-driven robust optimization framework is developed.•A systematic way to derive data-driven polyhedron uncertainty sets is proposed.•The power of PCA and kernel smoothing methods is leveraged for decision making.•The proposed framework includes both static and adaptive robust optimization.•Innovative applications on process control and operations under uncertainty.