Stochastic Mixed-Integer Programming is a mathematical optimization framework that integrates uncertainty modeled through stochastic processes with discrete decision variables, distinguishing it from deterministic or purely continuous stochastic programs by requiring both probabilistic constraints and integrality conditions on subsets of variables.
364
Publications
21.5K
Citations
942
Authors
408
Institutions
Publications per year
2017–2026
161
Variants
Smip
Parents
Publications
364
Introduction to Stochastic Programming
J M Wilson · Journal of the Operational Research Society · 1998 · 1.7K citations
1–5 of 364
Authors
942
- JP
11
h-index
- GH
7
h-index
- JC
7
h-index
- LN
7
h-index
- SS
6
h-index
1–5 of 942
Institutions
408
Texas A&M University
College Station, United States
11 h-index
22 publications
1.1K citations
University of Castilla-La Mancha
Ciudad Real, Spain
10 h-index
13 publications
568 citations
University of Michigan
Ann Arbor, United States
10 h-index
17 publications
703 citations
University of Beira Interior
Covilha, Portugal
9 h-index
20 publications
1.1K citations
Massachusetts Institute of Technology
Cambridge, United States
9 h-index
18 publications
1.4K citations
University of Lisbon
Lisbon, Portugal
9 h-index
14 publications
760 citations
Georgia Institute of Technology
Atlanta, United States
8 h-index
13 publications
866 citations
University of Regina
Regina, Canada
8 h-index
18 publications
750 citations
1–8 of 408
Venues
- IEEE Transactions on Power Systems
24 publications · 2.3K citations
22
h-index
- Mathematical Programming
14 publications · 1.5K citations
14
h-index
- International Journal of Production Research
13 publications · 659 citations
13
h-index
- Operations Research
13 publications · 987 citations
13
h-index
- European Journal of Operational Research
12 publications · 535 citations
12
h-index
1–5