Abstract
This paper deals with dynamic anticipatory adaptive control of airline seat reservation for the stochastic customer demand that occurs over time T before the flight is scheduled to depart. It is assumed that time T is divided into m periods, namely a full fare period and m−1 discounted fare periods. The fare structure is given. An airplane has a seat capacity of U. For the sake of simplicity, but without loss of generality, we consider (for illustration) the case of nonstop flight with two fare classes (business and economy). 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 airline seat reservation system to airline customer demand is carried out via the bivariate dependence of order statistics of cumulative customer demand. Dynamic anticipatory adaptive optimization of the airline seat allocation includes total dynamic anticipatory adaptive non-nested optimization of booking limits and local dynamic anticipatory adaptive nested optimization of protection levels over time T. It is carried out via the conditional predictability of order statistics. The airline seat reservation 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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Acknowledgments
This research was supported in part by Grant No. 06.1936 and Grant No. 07.2036 from the Latvian Council of Science and the National Institute of Mathematics and Informatics of Latvia.
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Nechval, N., Berzins, G., Danovics, V. (2017). A New Dynamic Model for Anticipatory Adaptive Control of Airline Seat Reservation via Order Statistics of Cumulative Customer Demand. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_41
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DOI: https://doi.org/10.1007/978-3-319-60042-0_41
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