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

• Main Authors: Bertsimas, Dimitris (Author), Mundru, Nishanth (Author)
• Format: Article
• Language: English
• Published: Institute for Operations Research and the Management Sciences (INFORMS), 2022-07-28T14:13:38Z.
• Subjects: Article
• Online Access: Get fulltext

 <jats:p> 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. </jats:p>