Abstract
Airline seat inventory control is a very profitable tool in the airline industry. The problem of adaptive stochastic airline seat inventory control lies at the heart of airline revenue management. This problem concerns the allocation of the finite seat inventory to the stochastic customer demand that occurs over time before the flight is scheduled to depart. The objective is to find the right combination of customers of various fare classes on the flight such that revenue is maximized. In this paper, the static and dynamic policies of stochastic airline seat inventory control (airline booking) are developed under parametric uncertainty of underlying models, which are not necessarily alternative. For the sake of simplicity, but without loss of generality, we consider (for illustration) the case of nonstop flights with two fare classes. The system developed is able to recognize a situation characterized by the number of reservations made by customers of the above fare classes at certain moment of time before departure. The proposed policies of the airline seat inventory control are based on the use of order statistics of cumulative customer demand, which have such properties as bivariate dependence and conditional predictability. Dynamic adaptation of the system to airline customer demand is carried out via the bivariate dependence of order statistics of cumulative customer demand. Dynamic optimization of the airline seat allocation is carried out via the conditional predictability of order statistics. The system makes on-line decisions as to whether to accept or reject any customer request using established decision rules based on order statistics of the current cumulative customer demand. The computer simulation results are promising.
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Nechval, N., Purgailis, M., Rozevskis, U., Nechval, K. (2013). Adaptive Stochastic Airline Seat Inventory Control under Parametric Uncertainty. In: Dudin, A., De Turck, K. (eds) Analytical and Stochastic Modeling Techniques and Applications. ASMTA 2013. Lecture Notes in Computer Science, vol 7984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39408-9_22
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