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Dynamic Pricing for Nonperishable Products with Demand Learning

Stern School of Business, New York University, New York, New York 10012, and Olayan School of Business, American University of Beirut, Beirut, Lebanon
Stern School of Business, New York University, New York, New York 10012
va03{at}aub.edu.lb
rcaldent{at}stern.nyu.edu

A retailer is endowed with a finite inventory of a nonperishable product. Demand for this product is driven by a price-sensitive Poisson process that depends on an unknown parameter that is a proxy for the market size. The retailer has a prior belief on the value of this parameter that he updates as time and available information (prices and sales) evolve. The retailer's objective is to maximize the discounted long-term average profits of his operation using dynamic pricing policies. We consider two cases. In the first case, the retailer is constrained to sell the entire initial stock of the nonperishable product before a different assortment is considered. In the second case, the retailer is able to stop selling the nonperishable product at any time and switch to a different menu of products. For both cases, we formulate the retailer's problem as a (Poisson) intensity control problem and derive structural properties of an optimal solution, and suggest a simple and efficient approximated solution. We use numerical computations, together with asymptotic analysis, to evaluate the performance of our proposed policy.

Subject classifications: dynamic pricing; Bayesian demand learning; approximations; intensity control; nonhomogeneous Poisson process; optimal stopping.
History: Received December 2005; revision received January 2009; accepted April 2009.