Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness
Chance-constrained programming (CCP) is one of the most difficult classes of optimization problems that has attracted the attention of researchers since the 1950s. In this survey, we focus on cases when only limited information on the distribution is available, such as a sample from the distribution...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | EURO Journal on Computational Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192440622000065 |
| _version_ | 1811201325379092480 |
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| author | Simge Küçükyavuz Ruiwei Jiang |
| author_facet | Simge Küçükyavuz Ruiwei Jiang |
| author_sort | Simge Küçükyavuz |
| collection | DOAJ |
| description | Chance-constrained programming (CCP) is one of the most difficult classes of optimization problems that has attracted the attention of researchers since the 1950s. In this survey, we focus on cases when only limited information on the distribution is available, such as a sample from the distribution, or the moments of the distribution. We first review recent developments in mixed-integer linear formulations of chance-constrained programs that arise from finite discrete distributions (or sample average approximation). We highlight successful reformulations and decomposition techniques that enable the solution of large-scale instances. We then review active research in distributionally robust CCP, which is a framework to address the ambiguity in the distribution of the random data. The focal point of our review is on scalable formulations that can be readily implemented with state-of-the-art optimization software. Furthermore, we highlight the prevalence of CCPs with a review of applications across multiple domains. |
| first_indexed | 2024-04-12T02:20:00Z |
| format | Article |
| id | doaj.art-76b3fdc31cf64bbdbd56c3e5d9814016 |
| institution | Directory Open Access Journal |
| issn | 2192-4406 |
| language | English |
| last_indexed | 2024-04-12T02:20:00Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EURO Journal on Computational Optimization |
| spelling | doaj.art-76b3fdc31cf64bbdbd56c3e5d98140162022-12-22T03:52:09ZengElsevierEURO Journal on Computational Optimization2192-44062022-01-0110100030Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustnessSimge Küçükyavuz0Ruiwei Jiang1Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, USA; Corresponding author.Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USAChance-constrained programming (CCP) is one of the most difficult classes of optimization problems that has attracted the attention of researchers since the 1950s. In this survey, we focus on cases when only limited information on the distribution is available, such as a sample from the distribution, or the moments of the distribution. We first review recent developments in mixed-integer linear formulations of chance-constrained programs that arise from finite discrete distributions (or sample average approximation). We highlight successful reformulations and decomposition techniques that enable the solution of large-scale instances. We then review active research in distributionally robust CCP, which is a framework to address the ambiguity in the distribution of the random data. The focal point of our review is on scalable formulations that can be readily implemented with state-of-the-art optimization software. Furthermore, we highlight the prevalence of CCPs with a review of applications across multiple domains.http://www.sciencedirect.com/science/article/pii/S2192440622000065Chance-constrained programsMixed-integer conic formulationsDistributionally robustAmbiguity setsApplications |
| spellingShingle | Simge Küçükyavuz Ruiwei Jiang Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness EURO Journal on Computational Optimization Chance-constrained programs Mixed-integer conic formulations Distributionally robust Ambiguity sets Applications |
| title | Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness |
| title_full | Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness |
| title_fullStr | Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness |
| title_full_unstemmed | Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness |
| title_short | Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness |
| title_sort | chance constrained optimization under limited distributional information a review of reformulations based on sampling and distributional robustness |
| topic | Chance-constrained programs Mixed-integer conic formulations Distributionally robust Ambiguity sets Applications |
| url | http://www.sciencedirect.com/science/article/pii/S2192440622000065 |
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