Abstract: Multiple booking interval difference discount gradually became an important branch of revenue management of airline ticket. In this paper, a new revenue management model are proposed and a dynamic iterative method to solve it. Firstly, the new revenue management model considering the price sensitivity and the demand transform among the booking interval. It is assumed that potential demand emerging in each specific booking interval can be predicted by big data method. Then the realistic demand can be formed as a function of the potential demand and price sensitivity and price discount. And the price sensitive is affected by the potential demand emerged in the interval and the demand transformed to it from the last interval. Secondly, a two-partition dynamic iterative method is presented to solve the model. In each iteration of the solving method, except the earliest one of the left booking intervals, all the others are treated as a temporary whole interval. Then a two-interval model is solved to determinate the price discount of the earliest booking interval. And then, drop that booking internal to the solved set and start another iteration until the last two intervals. Finally, two experimental examples are constructed. From the empirical results, this new pricing model conforms to the aviation ticket pricing practices. And some useful results are also proposed:(1) there is no monotonic linear relationship between the fare and the advance purchase time. (2) When the booking interval is closer to the flying date the optimal discount should be decreased. However, when the booking interval tend to reach the flying date the price discount should be increased again. (3) Price sensitivity is positively related to the price discount. (4) The transformation rate has a negative relationship with the discount. (5) The demand transformation can change the price sensitivity of the booking interval, which lead to impact the price discount. So, some beneficial reference is provided for airlines and passengers.

Key words: airline pricing, discount optimization, multiple booking interval, revenue management