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

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Capacity Uncertainty in Airline Revenue Management : Models, Algorithms, and Computations. / Busing, Christina; Kadatz, Daniel; Cleophas, Catherine.

In: Transportation Science, Vol. 53, No. 2, 01.03.2019, p. 383-400.

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Busing, Christina ; Kadatz, Daniel ; Cleophas, Catherine. / Capacity Uncertainty in Airline Revenue Management : Models, Algorithms, and Computations. In: Transportation Science. 2019 ; Vol. 53, No. 2. pp. 383-400.

Bibtex

@article{baf51416d511456e9f3f97106ae4d0b8,
title = "Capacity Uncertainty in Airline Revenue Management: Models, Algorithms, and Computations",
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.",
keywords = "revenue management, combinatorial optimisation, capacity uncertainty, overbooking, aviation",
author = "Christina Busing and Daniel Kadatz and Catherine Cleophas",
year = "2019",
month = mar
day = "1",
doi = "10.1287/trsc.2018.0829",
language = "English",
volume = "53",
pages = "383--400",
journal = "Transportation Science",
issn = "0041-1655",
publisher = "INFORMS",
number = "2",

}

RIS

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 -