Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization
Name
2020-mundru-optimization-based-scenario-reduction.pdf
Description
Submitted version
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442.42 KB
Format
Adobe PDF
Checksum (MD5)
8eda8812318d02bbaf93fb3a55a583a7
Author(s)
Bertsimas, Dimitris
Mundru, Nishanth
Date Issued
April 4, 2022
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Bertsimas, Dimitris and Mundru, Nishanth. 2022. "Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization." Operations Research.
Version
Original manuscript
Abstract
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.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/opre.2022.2265
