Robust Discrete Optimization

Robust Discrete Optimization is a formal optimization framework within decision-making under uncertainty that specifically addresses discrete decision spaces by seeking solutions which remain feasible and near-optimal across a range of possible future scenarios, distinguishing it from deterministic discrete optimization and other stochastic approaches by its emphasis on worst-case performance and immunization against uncertainty.

49

Publications

4.8K

Citations

128

Authors

79

Institutions

Publications per year

2017–2026

17

Authors

128

  1. AK
    Adam Kasperski

    Wrocław University of Science and Technology, Poland

    3 publications · 53 citations

    3

    h-index

  2. KN
    Karthik Natarajan

    National University of Singapore, Singapore

    3 publications · 103 citations

    3

    h-index

  3. SQ
    Shaojian Qu

    University of Shanghai for Science and Technology, China

    2 publications · 101 citations

    2

    h-index

  4. ZL
    Zuyi Li

    Illinois Institute of Technology, United States

    2 publications · 76 citations

    2

    h-index

  5. CM
    Changxi Ma

    Lanzhou Jiaotong University, China

    2 publications · 83 citations

    2

    h-index

1–5 of 128

Venues

  1. Annals of Operations Research

    4 publications · 289 citations

    4

    h-index

  2. Mathematical Programming

    4 publications · 1.9K citations

    4

    h-index

  3. Journal of the Operational Research Society

    OR · 3 publications · 1.1K citations

    3

    h-index

  4. Operations Research Letters

    3 publications · 139 citations

    3

    h-index

  5. IEEE Transactions on Power Systems

    3 publications · 127 citations

    3

    h-index

1–5