Dynamic Assortment Optimization

Dynamic Assortment Optimization is a formal framework in operations research and marketing science that dynamically determines the optimal subset of products to offer to consumers over time, distinguishing itself from static models by explicitly incorporating time-varying demand patterns, consumer learning, and adaptive behavior in response to changing market conditions.

32

Publications

2.4K

Citations

48

Authors

38

Institutions

Publications per year

2017–2026

20

Authors

48

  1. XC
    Xi Chen

    New York University, United States

    6 publications · 118 citations

    6

    h-index

  2. YW
    Yining Wang

    Carnegie Mellon University, United States

    6 publications · 118 citations

    6

    h-index

  3. AA
    Ali Aouad

    London Business School, United Kingdom

    5 publications · 146 citations

    5

    h-index

  4. DS
    Danny Segev

    Tel Aviv University, Israel

    4 publications · 116 citations

    4

    h-index

  5. DH
    Dorothée Honhon

    The University of Texas at Dallas, United States

    4 publications · 346 citations

    4

    h-index

1–5 of 48

Venues

  1. Operations Research

    8 publications · 1.1K citations

    8

    h-index

  2. SSRN Electronic Journal

    Repository · 6 publications · 106 citations

    6

    h-index

  3. Management Science

    4 publications · 648 citations

    4

    h-index

  4. Manufacturing & Service Operations Management

    4 publications · 297 citations

    4

    h-index

  5. arXiv (Cornell University)

    Repository · 3 publications · 54 citations

    3

    h-index

1–5