Robust Linear Programming

Robust Linear Programming is a mathematical optimization framework that extends linear programming by incorporating uncertainty in problem parameters, distinguishing itself through the incorporation of robustness constraints that ensure feasibility and optimality across all possible realizations within a defined uncertainty set.

196

Publications

23.6K

Citations

446

Authors

239

Institutions

Publications per year

2017–2026

67

Authors

446

  1. VJ
    V. Jeyakumar

    UNSW Sydney, Australia

    9 publications · 399 citations

    9

    h-index

  2. GL
    Guoyin Li

    UNSW Sydney, Australia

    7 publications · 366 citations

    7

    h-index

  3. MS
    Melvyn Sim

    National University of Singapore, Singapore

    7 publications · 6.4K citations

    7

    h-index

  4. AB
    Aharon Ben‐Tal

    Technion – Israel Institute of Technology, Israel

    7 publications · 6K citations

    7

    h-index

  5. AN
    Arkadi Nemirovski

    Technion – Israel Institute of Technology, Israel

    5 publications · 5.2K citations

    5

    h-index

1–5 of 446

Venues

  1. Mathematical Programming

    16 publications · 5.6K citations

    13

    h-index

  2. Operations Research

    8 publications · 5.7K citations

    8

    h-index

  3. Annals of Operations Research

    7 publications · 246 citations

    7

    h-index

  4. 6

    h-index

  5. Operations Research Letters

    6 publications · 2.4K citations

    6

    h-index

1–5