"Numerous industries use revenue management (RM) to forecast demand for products and to determine product prices and availability. Airline and hotel companies, who spearheaded RM developments in the 1990’s, have reported impressive annual revenue gains: American Airlines realized $500 million and Marriott realized $100 million. Today, however, the $218 billion airline and hotel sectors struggle to maintain profitability in a marketplace dominated by online purchases. Traditional RM systems have struggled to adapt to these new market conditions, leading to calls for fundamentally new “choice-based” RM systems that use discrete choice models to forecast demand in a way that better reflects today’s purchasing environment. Choice based revenue management has the potential to revolutionize the way that companies determine their pricing and revenue strategies. This approach incorporates consumer behavior into classical revenue management models. Customer behavior can be captured by utilizing the discrete-choice models. However, selecting the right choice model is a very challenging problem. Significant portion of the research topic of this proposal is dedicated to selecting the right choice model and efficiently estimating the choice model parameters. Second portion of this research is on integrating estimated consumer behavior into revenue management algorithms. Using the approaches described in this proposal, the effective product and consumer matching will yield the right set of products to be offered to right set of customers, at the right time and at the right price. These are timely research topics due to both their promise for significant advances in a variety of applications, as well as our recent initial progress on these problems. We have played a significant role in that initial progress, hence we are in a very good position to lead the solution of the problems posed in this project."