The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Distributionally Robust Optimization and Its Tractable Approximations
Abstract
In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible. Next, we develop new piecewise-linear decision rules that allow a more flexible reformulation of the original problem. The reformulated problem will generally contain terms with expectations on the positive parts of the recourse variables. Finally, we convert the uncertain linear program into a deterministic convex program by constructing distributionally robust bounds on these expectations. These bounds are constructed by first using different pieces of information on the distribution of the underlying uncertainties to develop separate bounds and next integrating them into a combined bound that is better than each of the individual bounds.
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Cites
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Optimization of conditional value-at-risk
Rockafellar, R. T., & Uryasev, S. (2000) · The Journal of Risk · doi:10.21314/jor.2000.038 · Cited by 6,725
Introduction to Stochastic Programming
Birge, J. R., & Louveaux, F. (2011) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/978-1-4614-0237-4 · Cited by 6,469
Bertsimas, D., & Sim, M. (2004) · Operations Research · doi:10.1287/opre.1030.0065 · Cited by 4,557
Self-citationBen-Tal, A., & Nemirovski, A. (1998) · Mathematics of Operations Research · doi:10.1287/moor.23.4.769 · Cited by 2,598
Stochastic Programming: The State of the Art In Honor of George B. Dantzig
(2010) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4419-1642-6 · Cited by 2,239
Delage, E., & Ye, Y. (2010) · Operations Research · doi:10.1287/opre.1090.0741 · Cited by 1,893
Robust solutions of uncertain linear programs
Ben-Tal, A., & Nemirovski, A. (1999) · Operations Research Letters · doi:10.1016/s0167-6377(99)00016-4 · Cited by 1,829
Soyster, A. L. (1973) · Operations Research · doi:10.1287/opre.21.5.1154 · Cited by 1,822
Robust solutions of Linear Programming problems contaminated with uncertain data
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Infrastructure planning for electric vehicles with battery swapping
Mak, H.-Y., Rong, Y., & Shen, Z.-J. M. (2013) · Management Science · doi:10.1287/mnsc.1120.1672 · Cited by 570
Robust Markov Decision Processes
Wiesemann, W., Kuhn, D., & Rustem, B. (2013) · Mathematics of Operations Research · doi:10.1287/moor.1120.0566 · Cited by 312
Mohajerin Esfahani, P., & Kuhn, D. (2018) · Mathematical Programming · doi:10.1007/s10107-017-1172-1 · Cited by 136
Metadata
- Authors
- Goh, J., & Sim, M.
- Venue
- Operations Research
- Published
- 2010-08
- Volume
- 58
- Issue
- 4-part-1
- Pages
- 902-917
- ISSN
- 0030-364X (ISSN-L)
- 1526-5463
- OpenAlex
- W2119303899
- Cited by
- 668
- References
- 34
- FWCI
- 25.29
- Type
- journal article
- Language
- en
- Open access
- closed
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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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