Mathematical Programming Models for Bid Prices and Competition in Airline Network Revenue Management



In the first part of the talk, we look at a multistage stochastic programming approach for generating bid prices in the airline network revenue management problem. This approach reflects the dynamic nature of the problem and does not assume that the decision maker has perfect information on future demand. We consider four different methodologies for multistage scenario tree generation (Monte-Carlo sampling, principal-component sampling, moment matching, and bootstrapping) and conclude that the sampling methods are best. Finally, our numerical results show that the multistage approach performs significantly better than the deterministic approach and that revenue managers who ignore demand uncertainty may be losing between 1% and 2% in average revenue. We next consider capacity competition between several airlines offering multiple products over a network. For such a system, using a variational inequality approach, we show the existence and uniqueness of Nash Equilibrium under certain regularity conditions. We then present numerical results for some small and large network examples and derive insights on how competition can affect quantities and prices for various players. We find that in the network case, competition can also affect these variables for products not using a common resource, and we also show how the model can be used to decide the value, with regards to making better revenue management decisions, of mergers between airlines.
Contact information:
Prof.dr. S.L. van de Velde