Essays on Revenue Management

Public Deposited

This thesis encompasses the work on bid-price controls for network revenue management and a dynamic model of revenue management with strategic consumers. The first line of research studies the circumstances under which bid-price controls are optimal or near optimal with minimal assumptions on the network topology and the stochastic structure of demand. In this general setting, in a discrete time model, I propose several novel bid-price control mechanisms and study their properties, proving that optimal bid prices form a martingale. To explore the martingale property further, I also consider a continuous time, rate-based model of network revenue management and show how an epsilon-optimal bid-price control and the corresponding bookings can be characterized as a solution to a Forward-Backward Stochastic Differential Equation (FBSDE). The analysis provides a new methodological approach to study revenue management problems by defining them as stochastic control problems and deriving the associated dual stochastic control problems. In the important special case of continuous information, machinery of FBSDE's and Ito calculus can be used to solve the network revenue management problem. Using the FBSDE connection, Malliavin calculus and Monte Carlo methods for solving FBSDE's can be utilized to compute near optimal bid prices. The second line of research incorporates the strategic consumer behavior in revenue management. I consider a dynamic model of revenue management with strategic consumers, where unlike in the classic revenue management literature, demand learning is the underlying process that leads to arrivals. In this setting, consumers learn their true valuations sequentially and a monopolist system manager tries to maximize her profits by sequentially screening the consumers. I identify the conditions under which the system manager can achieve the first-best solution. If these conditions are not satisfied, then the optimal mechanism is a menu of expiring refund contracts.

Last modified
  • 09/10/2018
Date created
Resource type
Rights statement