Capacity Uncertainty in Airline Revenue Management:Models, Algorithms, and Computations

Busing, Christina and Kadatz, Daniel and Cleophas, Catherine (2019) Capacity Uncertainty in Airline Revenue Management:Models, Algorithms, and Computations. Transportation Science, 53 (2). pp. 383-400. ISSN 0041-1655

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Abstract

Most airline revenue optimization models assume capacity to be fixed by fleet assignment, and thus treat it as deterministic. However, empirical data shows that on 40\% of flights, capacity is updated at least once within the booking horizon. Capacity updates can be caused by fleet-assignment re-optimizations or by short-term operational problems. This paper proposes a first model to integrate the resulting capacity uncertainty in the leg-based airline revenue management process. While assuming deterministic demand, the proposed model includes stochastic scenarios to represent potential capacity updates. To derive optimal inventory controls, we provide both a mixed-integer-program and a combinatorial solution approach, and discuss efficient ways of optimizing the special case of a single capacity update. We also explore effects of denied boarding cost and the model's relationship to the static overbooking problem. We numerically evaluate the model on empirically calibrated demand instances and benchmark it on the established deterministic approach and an upper bound based on perfect hindsight. In addition, we show that the combinatorial solution approach reduces the computational effort. Finally, we compare the static overbooking approach derived from the capacity uncertainty model to existing EMSR-based approaches.

Item Type:
Journal Article
Journal or Publication Title:
Transportation Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3313
Subjects:
ID Code:
123646
Deposited By:
Deposited On:
22 Feb 2018 16:52
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Jun 2020 04:49