Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization
提出一种新的基于优化的场景缩减方法，在求解两阶段随机优化问题时，通过考虑目标函数和问题结构来减少场景数量，相比现有方法在缩减至原样本1%-2%时表现更优，兼顾了计算可行性和可解释性。
In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used technique for computing a smaller number of scenarios to improve computational tractability and interpretability. However traditional approaches do not consider the decision quality when computing these scenarios. In “Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization,” Bertsimas and Mundru present a novel optimization-based method that explicitly considers the objective and problem structure for reducing the number of scenarios needed for solving two-stage stochastic optimization problems. This new proposed method is generally applicable and has significantly better performance when the number of reduced scenarios is 1%–2% of the full sample size compared with other state-of-the-art optimization and randomization methods, which suggests this improves both tractability and interpretability.