Home > Research > Publications & Outputs > The recoverable robust tail assignment problem

Links

Text available via DOI:

View graph of relations

The recoverable robust tail assignment problem

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

The recoverable robust tail assignment problem. / Froyland, Gary; Maher, Stephen; Wu, Cheng-Lung.
In: Transportation Science, Vol. 48, No. 3, 2014, p. 351-372.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Froyland, G, Maher, S & Wu, C-L 2014, 'The recoverable robust tail assignment problem', Transportation Science, vol. 48, no. 3, pp. 351-372. https://doi.org/10.1287/trsc.2013.0463

APA

Froyland, G., Maher, S., & Wu, C.-L. (2014). The recoverable robust tail assignment problem. Transportation Science, 48(3), 351-372. https://doi.org/10.1287/trsc.2013.0463

Vancouver

Froyland G, Maher S, Wu CL. The recoverable robust tail assignment problem. Transportation Science. 2014;48(3):351-372. Epub 2013 Jun 25. doi: 10.1287/trsc.2013.0463

Author

Froyland, Gary ; Maher, Stephen ; Wu, Cheng-Lung. / The recoverable robust tail assignment problem. In: Transportation Science. 2014 ; Vol. 48, No. 3. pp. 351-372.

Bibtex

@article{da1f7d319e3c4cf7b505d56efb2bddd6,
title = "The recoverable robust tail assignment problem",
abstract = "Schedule disruptions are commonplace in the airline industry with many flight-delaying events occurring each day. Recently there has been a focus on introducing robustness into airline planning stages to reduce the effect of these disruptions. We propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. We formulate the recoverable robust tail assignment problem (RRTAP) as a stochastic program, solved using column generation in the master and subproblems of the Benders' decomposition. We implement a two-phase algorithm for the Benders' decomposition and identify pareto-optimal cuts. The RRTAP includes costs due to flight delays, cancellation, and passenger rerouting, and the recovery stage includes cancellation, delay, and swapping options. To highlight the benefits of simultaneously solving planning and recovery problems in the RRTAP we compare our tail assignment solution against current approaches from the literature. Using airline data we demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption",
keywords = "robust airline optimisation, recovery, Benders{\textquoteright} decomposition",
author = "Gary Froyland and Stephen Maher and Cheng-Lung Wu",
year = "2014",
doi = "10.1287/trsc.2013.0463",
language = "English",
volume = "48",
pages = "351--372",
journal = "Transportation Science",
issn = "0041-1655",
publisher = "INFORMS",
number = "3",

}

RIS

TY - JOUR

T1 - The recoverable robust tail assignment problem

AU - Froyland, Gary

AU - Maher, Stephen

AU - Wu, Cheng-Lung

PY - 2014

Y1 - 2014

N2 - Schedule disruptions are commonplace in the airline industry with many flight-delaying events occurring each day. Recently there has been a focus on introducing robustness into airline planning stages to reduce the effect of these disruptions. We propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. We formulate the recoverable robust tail assignment problem (RRTAP) as a stochastic program, solved using column generation in the master and subproblems of the Benders' decomposition. We implement a two-phase algorithm for the Benders' decomposition and identify pareto-optimal cuts. The RRTAP includes costs due to flight delays, cancellation, and passenger rerouting, and the recovery stage includes cancellation, delay, and swapping options. To highlight the benefits of simultaneously solving planning and recovery problems in the RRTAP we compare our tail assignment solution against current approaches from the literature. Using airline data we demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption

AB - Schedule disruptions are commonplace in the airline industry with many flight-delaying events occurring each day. Recently there has been a focus on introducing robustness into airline planning stages to reduce the effect of these disruptions. We propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. We formulate the recoverable robust tail assignment problem (RRTAP) as a stochastic program, solved using column generation in the master and subproblems of the Benders' decomposition. We implement a two-phase algorithm for the Benders' decomposition and identify pareto-optimal cuts. The RRTAP includes costs due to flight delays, cancellation, and passenger rerouting, and the recovery stage includes cancellation, delay, and swapping options. To highlight the benefits of simultaneously solving planning and recovery problems in the RRTAP we compare our tail assignment solution against current approaches from the literature. Using airline data we demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption

KW - robust airline optimisation

KW - recovery

KW - Benders’ decomposition

U2 - 10.1287/trsc.2013.0463

DO - 10.1287/trsc.2013.0463

M3 - Journal article

VL - 48

SP - 351

EP - 372

JO - Transportation Science

JF - Transportation Science

SN - 0041-1655

IS - 3

ER -