Dynamic pricing and inventory control with learning

Nicholas C. Petruzzi, Maqbool Dada

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Optimal operating policies and corresponding managerial insight are developed for the decision problem of coordinating supply and demand when (i) both supply and demand can be influenced by the decision maker and (ii) learning is pursued. In particular, we determine optimal stocking and pricing policies over time when a given market parameter of the demand process, though fixed, initially is unknown. Because of the initially unknown market parameter, the decision maker begins the problem horizon with a subjective probability distribution associated with demand. Learning occurs as the firm monitors the market's response to its decisions and then updates its characterization of the demand function. Of primary interest is the effect of censored data since a firm's observations often are restricted to sales. We find that the first-period optimal selling price increases with the length of the problem horizon. However, for a given problem horizon, prices can rise or fall over time, depending on how the scale parameter influences demand. Further results include the characterization of the optimal stocking quantity decision and a computationally viable algorithm.

Original languageEnglish (US)
Pages (from-to)303-325
Number of pages23
JournalNaval Research Logistics
Issue number3
StatePublished - Apr 2002
Externally publishedYes


  • Censored demand
  • Demand updating
  • Inventory management
  • Uncertainty

ASJC Scopus subject areas

  • Modeling and Simulation
  • Ocean Engineering
  • Management Science and Operations Research


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