Revenue Management

This project focuses on optimizing airline revenue using stochastic dynamic decision models. By modeling demand uncertainty, fare class structures, and capacity constraints, these models enable airlines to make data-driven decisions on pricing, seat allocation, and overbooking strategies. The stochastic framework accounts for variability in customer booking behaviors and external factors, while dynamic programming techniques help identify optimal policies over time. The goal is to maximize revenue by balancing the trade-off between early sales at lower fares and the potential for higher-yield bookings closer to departure.