Robust Optimization
Robust Optimization is a prescriptive analytics methodology within optimization theory that distinguishes itself by seeking solutions that remain feasible and near-optimal under parameter uncertainty, explicitly accounting for worst-case scenarios within predefined uncertainty sets.
9.6K
Publications
576.5K
Citations
18.1K
Authors
3.3K
Institutions
Publications per year
2017–2026
3.9K
9.6K
Automatica · 1997 · 5.5K citations
Dimitris Bertsimas, Melvyn Sim · Operations Research · 2004 · 4.4K citations
Kemin Zhou, John C. Doyle, K. Glover · 1997 · 4.4K citations
John C. Doyle · 2002 · 3.7K citations
1–5 of 9.6K
18.1K
Massachusetts Institute of Technology, United States
50 publications · 16.2K citations
36
h-index
35
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26
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24
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24
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1–5 of 18.1K
3.3K
Massachusetts Institute of Technology
Cambridge, United States
64 h-index
307 publications
40.8K citations
Tsinghua University
Beijing, China
62 h-index
537 publications
33.5K citations
National University of Singapore
Singapore, Singapore
55 h-index
265 publications
22.2K citations

North China Electric Power University
Beijing, China
54 h-index
410 publications
22.9K citations
Stanford University
Stanford, United States
51 h-index
212 publications
21.9K citations
Georgia Institute of Technology
Atlanta, United States
46 h-index
204 publications
17K citations
Technion – Israel Institute of Technology
Haifa, Israel
45 h-index
128 publications
30.4K citations

Centre National de la Recherche Scientifique
Paris, France
44 h-index
225 publications
15.1K citations
1–8 of 3.3K
236 publications · 24.9K citations
90
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191 publications · 19K citations
77
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229 publications · 14.8K citations
69
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147 publications · 20.9K citations
62
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101 publications · 9K citations
58
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1–5