Markdown Optimization Under Uncertainty

Markdown Optimization Under Uncertainty is a decision-theoretic framework that formalizes the problem of dynamically setting markdown pricing strategies under incomplete information about demand, competition, or market response, distinguishing it from deterministic markdown optimization approaches by explicitly modeling and addressing stochasticity and ambiguity in revenue management.

11

Publications

626

Citations

29

Authors

19

Institutions

Publications per year

2017–2026

2

Variants

Stochastic Markdown Optimization

Authors

29

  1. AM
    Alan Mercer

    Lancaster University, United Kingdom

    1 publications · 22 citations

    1

    h-index

  2. JD
    Jonathan D. Evans

    University of Bath, United Kingdom

    1 publications · 14 citations

    1

    h-index

  3. RC
    Roger C. Baker

    Brigham Young University, United States

    1 publications · 144 citations

    1

    h-index

  4. BP
    Bruce Preston

    Columbia University, United States

    1 publications · 14 citations

    1

    h-index

  5. JC
    John C. Yi

    Saint Joseph's University, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 29

Venues

  1. European Journal of Operational Research

    2 publications · 166 citations

    2

    h-index

  2. Computers & Industrial Engineering

    2 publications · 62 citations

    2

    h-index

  3. Manufacturing & Service Operations Management

    1 publications · 115 citations

    1

    h-index

  4. Naval Research Logistics (NRL)

    1 publications · 28 citations

    1

    h-index

  5. IIE Transactions

    1 publications · 15 citations

    1

    h-index

1–5