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
Parents
11
R. Ghasemy Yaghin, S. Ali Torabi, S.M.T. Fatemi Ghomi · Applied Mathematical Modelling · 2012 · 67 citations
Management, Operations Management, Supply Chain Management +17
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29
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19
Amirkabir University of Technology
Tehran, Iran
1 h-index
2 publications
134 citations
University of Colorado Boulder
Boulder, United States
1 h-index
1 publications
115 citations
Blue Yonder (United States)
Scottsdale, United States
1 h-index
1 publications
15 citations

California State University, Long Beach
Long Beach, United States
1 h-index
1 publications
15 citations
The University of Texas at Arlington
Arlington, United States
1 h-index
1 publications
144 citations
Walmart (United States)
Bentonville, United States
1 h-index
1 publications
15 citations

University of Tehran
Tehran, Iran
1 h-index
1 publications
67 citations
University of Maryland, College Park
College Park, United States
1 h-index
1 publications
15 citations
1–8 of 19
2 publications · 166 citations
2
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2 publications · 62 citations
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1 publications · 115 citations
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1 publications · 28 citations
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1 publications · 15 citations
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