Risk-Averse Optimization is a stochastic optimization framework that incorporates risk measures or risk preferences into the objective function or constraints to systematically favor solutions that perform reliably under uncertainty, distinguishing it from classical stochastic programming by explicitly penalizing variability and downside risk in random outcomes.
178
Publications
8.3K
Citations
510
Authors
278
Institutions
Publications per year
2017–2026
107
Publications
178
1–5 of 178
Authors
510
- AJ
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- YX
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1–5 of 510
Institutions
278
Nanyang Technological University
Singapore, Singapore
7 h-index
13 publications
1.2K citations
Hong Kong Polytechnic University
Hong Kong, Hong Kong
7 h-index
10 publications
617 citations
Stevens Institute of Technology
Hoboken, United States
5 h-index
8 publications
472 citations
Tsinghua University
Beijing, China
5 h-index
9 publications
594 citations
The Ohio State University
Columbus, United States
4 h-index
5 publications
122 citations
Shanghai Jiao Tong University
Shanghai, China
4 h-index
8 publications
233 citations
University of Bath
Bath, United Kingdom
4 h-index
7 publications
279 citations
Sichuan University
Chengdu, China
4 h-index
6 publications
313 citations
1–8 of 278
Venues
- International Journal of Production Research
8 publications · 542 citations
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- IEEE Transactions on Power Systems
8 publications · 850 citations
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- Applied Energy
7 publications · 421 citations
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- Energy
8 publications · 414 citations
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- European Journal of Operational Research
4 publications · 363 citations
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