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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Capacity Uncertainty in Airline Revenue Management
T2 - Models, Algorithms, and Computations
AU - Busing, Christina
AU - Kadatz, Daniel
AU - Cleophas, Catherine
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
KW - revenue management
KW - combinatorial optimisation
KW - capacity uncertainty
KW - overbooking
KW - aviation
U2 - 10.1287/trsc.2018.0829
DO - 10.1287/trsc.2018.0829
M3 - Journal article
VL - 53
SP - 383
EP - 400
JO - Transportation Science
JF - Transportation Science
SN - 0041-1655
IS - 2
ER -