Revenue Management with Incomplete Demand Information

Consider a seller who is endowed with a fixed number of units of a product that he/she can sell to a price-sensitive and stochastically arriving stream of consumers during a finite time horizon. The seller has incomplete demand information, that is, there are some characteristics of the demand process (e.g., the arrival rate or the price elasticity) that he/she does not know with certainty. The seller's problem is to dynamically adjust the product's price to maximize the expected revenues he/she can collect if no replenishment is possible during the selling season. Keywords: revenue management; dynamic pricing; Bayesian learning; parametric and nonparametric learning; approximations; Poisson intensity control