Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization

In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used technique for computing smaller number scenarios to improve computational tractability and interpretability. However traditional approaches do not consider decision quality when these scenarios. “Optimization-Based Scenario Reduction Data-Driven Two-Stage Stochastic Optimization,” Bertsimas Mundru present novel optimization-based method that explicitly considers objective problem structure reducing needed solving two-stage stochastic problems. This new proposed generally applicable has significantly better performance reduced 1%–2% full sample size compared with other state-of-the-art randomization methods, which suggests this improves both