In this dissertation, I study two topics in the context of revenue management. The ¯rst topic
involves building a mathematical model to analyze the competition between many retailers
who can change the price of their respective products in real time. I develop a game-theoretic
model for the dynamic price competition where each retailer's objective is to maximize its
own expected total revenue. I use the Nash equilibrium to predict market equilibrium and
provide managerial insights into how each retailer should take into account its competitors'
behavior when setting the price.
The second topic involves working with Amtrak, the national railroad passenger corpo-
ration, to develop a revenue management model. The revenue management department of
Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to
bring their vehicles on the train. I analyze the demand structure from sales data and build a
mathematical model to describe the sales process for Auto Train. I further develop an algorithm to calculate the optimal pricing strategy that yields the maximum revenue. Because of
the distinctive service provided by Auto Train, my findings make important contribution to
the revenue management literature.
