Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization
| Autor: | Dimitris Bertsimas, Nishanth Mundru |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Operations Research. |
| ISSN: | 1526-5463 0030-364X |
| DOI: | 10.1287/opre.2022.2265 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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