Stochastic Dynamic Optimization

Stochastic Dynamic Optimization is a mathematical framework for modeling sequential decision-making processes under uncertainty, where optimal policies are derived by maximizing expected cumulative rewards over time subject to stochastic state transitions.

532

Publications

28.9K

Citations

1.2K

Authors

539

Institutions

Publications per year

2017–2026

148

Authors

1.2K

  1. WB
    Warren B. Powell

    Princeton University, United States

    8 publications · 513 citations

    8

    h-index

  2. MC
    Michael C. Caramanis

    Boston University, United States

    6 publications · 198 citations

    6

    h-index

  3. BC
    Boxiao Chen

    University of Illinois Chicago, United States

    5 publications · 169 citations

    5

    h-index

  4. YW
    Yining Wang

    Carnegie Mellon University, United States

    5 publications · 169 citations

    5

    h-index

  5. MJ
    Michael J. Neely

    University of Southern California, United States

    4 publications · 194 citations

    4

    h-index

1–5 of 1.2K

Venues

  1. Operations Research

    26 publications · 2.4K citations

    23

    h-index

  2. European Journal of Operational Research

    26 publications · 1.2K citations

    21

    h-index

  3. Management Science

    16 publications · 1.2K citations

    14

    h-index

  4. arXiv (Cornell University)

    Repository · 11 publications · 184 citations

    10

    h-index

  5. SSRN Electronic Journal

    Repository · 10 publications · 234 citations

    10

    h-index

1–5