Robust Stochastic Programming
Robust Stochastic Programming is a modeling framework in stochastic optimization that integrates robust optimization principles to handle uncertainty by seeking solutions that remain feasible and optimal under a set of plausible stochastic scenarios, thereby emphasizing resilience against distributional ambiguity and model misspecification.
179
Publications
13.8K
Citations
521
Authors
266
Institutions
Publications per year
2017–2026
107
Parents
179
1–5 of 179
521
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266

Iran University of Science and Technology
Tehran, Iran
12 h-index
19 publications
2.2K citations
Tsinghua University
Beijing, China
7 h-index
20 publications
1K citations
University of Tabriz
Tabriz, Iran
7 h-index
20 publications
1K citations

Kharazmi University
Tehran, Iran
7 h-index
15 publications
502 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
13 publications
699 citations

Yazd University
Yazd, Iran
5 h-index
6 publications
329 citations

King Abdulaziz University
Jeddah, Saudi Arabia
5 h-index
5 publications
319 citations

Tarbiat Modares University
Tehran, Iran
5 h-index
9 publications
501 citations
1–8 of 266
6 publications · 661 citations
6
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6 publications · 351 citations
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6 publications · 2.4K citations
6
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6 publications · 401 citations
6
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6 publications · 331 citations
5
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1–5