Robust Stochastic Programming

Robust Stochastic Programming is a modeling framework in stochastic optimization that integrates robust optimization principles to handle uncertainty by seeking solutions that remain feasible and optimal under a set of plausible stochastic scenarios, thereby emphasizing resilience against distributional ambiguity and model misspecification.

179

Publications

13.8K

Citations

521

Authors

266

Institutions

Publications per year

2017–2026

107

Authors

521

  1. MS
    Miadreza Shafie‐khah

    Universidade do Porto, Portugal

    5 publications · 245 citations

    5

    h-index

  2. RL
    Reza Lotfi

    Yazd University, Iran

    5 publications · 243 citations

    5

    h-index

  3. GH
    Guohe Huang

    University of Regina, Canada

    5 publications · 147 citations

    5

    h-index

  4. WW
    Wei Wei

    Tsinghua University, China

    5 publications · 242 citations

    5

    h-index

  5. BM
    Behnam Mohammadi‐Ivatloo

    University of Tabriz, Iran

    5 publications · 287 citations

    5

    h-index

1–5 of 521

Venues

  1. Journal of Cleaner Production

    6 publications · 661 citations

    6

    h-index

  2. Energy

    6 publications · 351 citations

    6

    h-index

  3. Operations Research

    6 publications · 2.4K citations

    6

    h-index

  4. Applied Energy

    6 publications · 401 citations

    6

    h-index

  5. Annals of Operations Research

    6 publications · 331 citations

    5

    h-index

1–5