The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Optimal scenario reduction for one- and two-stage robust optimization with discrete uncertainty in the objective
Open accessAbstract
Robust optimization typically follows a worst-case perspective, where a single scenario may determine the objective value of a given solution. Accordingly, it is a challenging task to reduce the size of an uncertainty set without changing the resulting objective value too much. On the other hand, robust optimization problems with many scenarios tend to be hard to solve, in particular for two-stage problems. Hence, a reduced uncertainty set may be central to find solutions in reasonable time. We propose scenario reduction methods that give guarantees on the performance of the resulting robust solution. Scenario reduction problems for one- and two-stage robust optimization are framed as optimization problems that only depend on the uncertainty set and not on the underlying decision making problem. Experimental results indicate that objective values for the reduced uncertainty sets are closely correlated to original objective values, resulting in better solutions than when using general-purpose clustering methods such as K-means.
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Cites
Scenario reduction in stochastic programming
Dupa?ov�, J., Gr�we-Kuska, N., & R�misch, W. (2003) · Mathematical Programming · doi:10.1007/s10107-002-0331-0 · Cited by 1,041
Scenario Reduction Algorithms in Stochastic Programming
Heitsch, H., & Römisch, W. (2003) · Computational Optimization and Applications · doi:10.1023/a:1021805924152 · Cited by 982
Min–max and min–max regret versions of combinatorial optimization problems: A survey
Aissi, H., Bazgan, C., & Vanderpooten, D. (2008) · European Journal of Operational Research · doi:10.1016/j.ejor.2008.09.012 · Cited by 498
A survey of adjustable robust optimization
Yanıkoğlu, İ., Gorissen, B. L., & den Hertog, D. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.08.031 · Cited by 347
A unified framework for stochastic optimization
Powell, W. B. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.07.014 · Cited by 314
On the complexity of a class of combinatorial optimization problems with uncertainty
Averbakh, I. (2001) · Mathematical Programming · doi:10.1007/pl00011424 · Cited by 162
Robustness Analysis in Decision Aiding, Optimization, and Analytics
Doumpos, M., Zopounidis, C., & Grigoroudis, E. (2016) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-33121-8 · Cited by 115
Robust Discrete Optimization Under Discrete and Interval Uncertainty: A Survey
Kasperski, A., & Zieliński, P. (2016) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-33121-8_6 · Cited by 97
Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization
Bertsimas, D., & Mundru, N. (2022) · Operations Research · doi:10.1287/opre.2022.2265 · Cited by 83
Algorithms and uncertainty sets for data-driven robust shortest path problems
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A machine learning approach to two-stage adaptive robust optimization
Bertsimas, D., & Kim, C. W. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.06.012 · Cited by 23
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Data-driven prediction of relevant scenarios for robust combinatorial optimization
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Metadata
- Authors
- Goerigk, M., & Khosravi, M.
- Affiliations
- University of Siegen (DE)
- Corresponding author
- Goerigk, Marc
- ORCID
- Goerigk, Marc
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2023-10
- Volume
- 310
- Issue
- 2
- Pages
- 529-551
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W4327924010
- Cited by
- 20
- References
- 23
- Citations by year
- 2026: 9 · 2025: 3 · 2024: 7 · 2023: 1
- FWCI
- 2.96
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- European Journal of Operational Research (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (GO 2069/1-1)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Keywords
- scenario reduction, robust optimization, two-stage robust optimization, worst-case scenario, performance guarantees, clustering
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- Goerigk, M., & Khosravi, M. (2023). Optimal scenario reduction for one- and two-stage robust optimization with discrete uncertainty in the objective. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2023.03.019
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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