Data-driven Optimization

Data-Driven Optimization is an optimization framework that belongs to the class of methods for decision-making under uncertainty and is distinguished by its reliance on empirical data—rather than explicit probabilistic models or assumptions—to directly inform and shape the optimization process.

597

Publications

34K

Citations

2.3K

Authors

757

Institutions

Publications per year

2017–2026

487

Authors

2.3K

  1. DB
    Dimitris Bertsimas

    Massachusetts Institute of Technology, United States

    16 publications · 1.6K citations

    14

    h-index

  2. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    10 publications · 1.7K citations

    10

    h-index

  3. FY
    Fengqi You

    Cornell University, United States

    10 publications · 1.1K citations

    10

    h-index

  4. LZ
    Liang Zhao

    East China University of Science and Technology, China

    10 publications · 395 citations

    10

    h-index

  5. HW
    Handing Wang

    Xidian University, China

    8 publications · 1.6K citations

    8

    h-index

1–5 of 2.3K

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 1K citations

    15

    h-index

  2. Computers & Chemical Engineering

    15 publications · 1K citations

    13

    h-index

  3. Management Science

    10 publications · 542 citations

    10

    h-index

  4. Applied Energy

    11 publications · 585 citations

    10

    h-index

  5. Operations Research

    9 publications · 508 citations

    9

    h-index

1–5