Improved Airline Seat Inventory Control Policies under Parametric Uncertainty of Customer Demand Models

Abstract

Most models, which are used for solving airline seat inventory control problems, are developed in the literature under the assumptions that the parameter values of the models are known with certainty. When these models are applied to solve real-world problems, the parameters are estimated and then treated as if they were the true values. The risk associated with using estimates rather than the true parameters is called estimation risk and is often ignored. When data are limited and/or unreliable, estimation risk may be significant, and failure to incorporate it into the model design may lead to serious errors. In this paper, we consider the static and dynamic problems of airline seat inventory control under parametric uncertainty, which are invariant with respect to a certain group of transformations. Since common practice for airlines is to charge several different fares for a common pool of seats, this paper presents the policies that have been used to address the problem of when to refuse booking requests for a given fare level to save the seat for a potential request at a higher fare level. In this paper,we present the innovative technologies for constructing the static and dynamic policies of the airline seat inventory control.on the basis of the ‘unbiasedness performance index’. The idea of prediction of a future cumulative customer demand for the seats on a flight via the order statistics from the underlying distribution, introduced in the paper, allows one to use the invariant embedding technique in order to eliminate the unknown parameters from the problem and to use the previous and current sample data as completely as possible. The proposed unbiased static and dynamic policies are more efficient as compared with the policies, where the unknown parameters of the airline customer demand models are estimated and then treated as if they were the true values. An illustrative example is given.