Robust Optimization

Robust Optimization is a prescriptive analytics methodology within optimization theory that distinguishes itself by seeking solutions that remain feasible and near-optimal under parameter uncertainty, explicitly accounting for worst-case scenarios within predefined uncertainty sets.

9.6K

Publications

576.5K

Citations

18.1K

Authors

3.3K

Institutions

Publications per year

2017–2026

3.9K

Authors

18.1K

  1. DB
    Dimitris Bertsimas

    Massachusetts Institute of Technology, United States

    50 publications · 16.2K citations

    36

    h-index

  2. GH
    Guohe Huang

    University of Regina, Canada

    72 publications · 3.3K citations

    35

    h-index

  3. FY
    Fengqi You

    Cornell University, United States

    29 publications · 2.7K citations

    26

    h-index

  4. YL
    Yongping Li

    University of Regina, Canada

    38 publications · 1.7K citations

    24

    h-index

  5. WW
    Wei Wei

    Tsinghua University, China

    31 publications · 2.5K citations

    24

    h-index

1–5 of 18.1K

Venues

  1. IEEE Transactions on Power Systems

    236 publications · 24.9K citations

    90

    h-index

  2. IEEE Transactions on Signal Processing

    191 publications · 19K citations

    77

    h-index

  3. European Journal of Operational Research

    229 publications · 14.8K citations

    69

    h-index

  4. Mathematical Programming

    147 publications · 20.9K citations

    62

    h-index

  5. IEEE Transactions on Smart Grid

    101 publications · 9K citations

    58

    h-index

1–5