Optimising airline overbooking using a hybrid gradient approach and statistical modelling

We develop an overbooking approach for airline revenue management. We estimate a revenue function by employing a statistical modelling approach, specifically a multivariate adaptive regression splines approximation of a stochastic network model. We develop an overbooking cost function using a binomial distribution to model the number of customers that show up for the flight. We implement a hybrid gradient algorithm that combines Newton's and a steepest ascent method to optimise profit. Finally, we compare our method to one that overbooks based on the probability that the number of customers that show up for a flight exceeds the flight capacity.