Stochastic Programming

Stochastic Programming is a branch of mathematical optimization that extends deterministic programming models by incorporating random parameters into the decision-making process, thereby enabling the formulation of optimal decisions under uncertainty through the minimization of expected costs or risks over multiple scenarios.

6.4K

Publications

460.7K

Citations

10.7K

Authors

2.5K

Institutions

Publications per year

2017–2026

2.1K

Authors

10.7K

  1. GH
    Guohe Huang

    University of Regina, Canada

    124 publications · 6.5K citations

    48

    h-index

  2. AS
    Alexander Shapiro

    Georgia Institute of Technology, United States

    46 publications · 8.4K citations

    33

    h-index

  3. RJ
    Roger J.‐B. Wets

    University of California, Davis, United States

    37 publications · 5.5K citations

    30

    h-index

  4. YL
    Yongping Li

    University of Regina, Canada

    58 publications · 2.8K citations

    28

    h-index

  5. MS
    Mohammad Shahidehpour

    Illinois Institute of Technology, United States

    29 publications · 4K citations

    26

    h-index

1–5 of 10.7K

Venues

  1. IEEE Transactions on Power Systems

    220 publications · 27K citations

    96

    h-index

  2. Operations Research

    212 publications · 24.4K citations

    82

    h-index

  3. European Journal of Operational Research

    235 publications · 16.4K citations

    71

    h-index

  4. Mathematical Programming

    148 publications · 16.9K citations

    68

    h-index

  5. IEEE Transactions on Smart Grid

    65 publications · 8.1K citations

    52

    h-index

1–5