Stochastic Linear Programming is a mathematical optimization framework within the class of linear programming models that incorporates random parameters in the coefficients or constraints to account for uncertainty, thereby distinguishing it from deterministic linear programs by explicitly modeling probabilistic variations in problem data.
384
Publications
26K
Citations
714
Authors
358
Institutions
Publications per year
2017–2026
59
Variants
Stochastic Linear Programs
Publications
384
1–5 of 384
Authors
714
- JR
12
h-index
- SS
10
h-index
- MS
8
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- RJ
6
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- MA
6
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1–5 of 714
Institutions
358
University of Michigan
Ann Arbor, United States
13 h-index
17 publications
2.4K citations
Hiroshima University
Hiroshima, Japan
9 h-index
20 publications
564 citations
University of Arizona
Tucson, United States
9 h-index
17 publications
1.9K citations
University of Waterloo
Waterloo, Canada
8 h-index
16 publications
1.2K citations
University of Illinois Urbana-Champaign
Urbana, United States
8 h-index
10 publications
369 citations
The University of Texas at Austin
Austin, United States
7 h-index
11 publications
572 citations
Cornell University
Ithaca, United States
7 h-index
8 publications
490 citations
Stanford University
Stanford, United States
7 h-index
9 publications
928 citations
1–8 of 358
Venues
- Operations Research
26 publications · 4.3K citations
23
h-index
- European Journal of Operational Research
22 publications · 1.7K citations
18
h-index
- Management Science
18 publications · 1.9K citations
16
h-index
- Mathematical Programming
17 publications · 1.4K citations
16
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- Mathematics of Operations Research
11 publications · 1.3K citations
11
h-index
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