Optimization Under Uncertainty

Optimization Under Uncertainty is a mathematical optimization paradigm that systematically accounts for indeterminate parameters or stochastic variations in design variables, objectives, or constraints, distinguishing itself from deterministic optimization by explicitly incorporating probabilistic, interval-based, or scenario-driven representations of uncertainty to achieve robust or reliable optimal solutions.

11.7K

Publications

658.6K

Citations

20.3K

Authors

3.6K

Institutions

Publications per year

2017–2026

4.7K

Authors

20.3K

  1. GH
    Guohe Huang

    University of Regina, Canada

    212 publications · 10.7K citations

    53

    h-index

  2. YL
    Yongping Li

    University of Regina, Canada

    105 publications · 4.2K citations

    36

    h-index

  3. JW
    Jianhui Wang

    Argonne National Laboratory, United States

    37 publications · 6.1K citations

    32

    h-index

  4. DB
    Dimitris Bertsimas

    Massachusetts Institute of Technology, United States

    42 publications · 12.7K citations

    31

    h-index

  5. FY
    Fengqi You

    Cornell University, United States

    31 publications · 3.1K citations

    29

    h-index

1–5 of 20.3K

Venues

  1. IEEE Transactions on Power Systems

    411 publications · 43K citations

    116

    h-index

  2. European Journal of Operational Research

    227 publications · 17.4K citations

    69

    h-index

  3. IEEE Transactions on Automatic Control

    183 publications · 18.9K citations

    67

    h-index

  4. Operations Research

    145 publications · 25.5K citations

    67

    h-index

  5. IEEE Transactions on Smart Grid

    120 publications · 10.1K citations

    61

    h-index

1–5