Robust Integer Programming

Robust Integer Programming is a mathematical optimization framework that belongs to the class of integer programming problems, distinguished by its explicit incorporation of uncertainty in problem parameters through robust optimization techniques to ensure feasible and optimal solutions under all realizations within a defined uncertainty set.

67

Publications

5.2K

Citations

196

Authors

117

Institutions

Publications per year

2017–2026

30

Authors

196

  1. YJ
    Ying Ji

    University of Shanghai for Science and Technology, China

    3 publications · 138 citations

    3

    h-index

  2. AG
    Ali Ghodratnama

    Kharazmi University, Iran

    3 publications · 118 citations

    3

    h-index

  3. JW
    Jianhui Wang

    Argonne National Laboratory, United States

    3 publications · 648 citations

    3

    h-index

  4. CZ
    Chaoyue Zhao

    Oklahoma State University, United States

    3 publications · 661 citations

    3

    h-index

  5. YG
    Yongpei Guan

    University of Florida, United States

    3 publications · 620 citations

    3

    h-index

1–5 of 196

Venues

  1. European Journal of Operational Research

    4 publications · 390 citations

    4

    h-index

  2. IEEE Transactions on Power Systems

    4 publications · 707 citations

    4

    h-index

  3. Journal of the Operational Research Society

    OR · 3 publications · 1.1K citations

    3

    h-index

  4. 2

    h-index

  5. Journal of Cleaner Production

    2 publications · 76 citations

    2

    h-index

1–5