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A two-stage stochastic integer programming model for air traffic flow management

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A two-stage stochastic integer programming model for air traffic flow management. / Corolli, Luca; Lulli, Guglielmo; Ntaimo, Lewis et al.
In: IMA Journal of Management Mathematics, Vol. 28, No. 1, 01.2017, p. 19-40.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Corolli, L, Lulli, G, Ntaimo, L & Venkatachalam, S 2017, 'A two-stage stochastic integer programming model for air traffic flow management', IMA Journal of Management Mathematics, vol. 28, no. 1, pp. 19-40. https://doi.org/10.1093/imaman/dpv017

APA

Corolli, L., Lulli, G., Ntaimo, L., & Venkatachalam, S. (2017). A two-stage stochastic integer programming model for air traffic flow management. IMA Journal of Management Mathematics, 28(1), 19-40. https://doi.org/10.1093/imaman/dpv017

Vancouver

Corolli L, Lulli G, Ntaimo L, Venkatachalam S. A two-stage stochastic integer programming model for air traffic flow management. IMA Journal of Management Mathematics. 2017 Jan;28(1):19-40. Epub 2015 May 22. doi: 10.1093/imaman/dpv017

Author

Corolli, Luca ; Lulli, Guglielmo ; Ntaimo, Lewis et al. / A two-stage stochastic integer programming model for air traffic flow management. In: IMA Journal of Management Mathematics. 2017 ; Vol. 28, No. 1. pp. 19-40.

Bibtex

@article{602d8ba07f0c4d3aa1a65f226ec6f05a,
title = "A two-stage stochastic integer programming model for air traffic flow management",
abstract = "The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting.To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines.",
keywords = "air traffic flow management, stochastic integer programming, uncertainty, progressivebinary heuristic",
author = "Luca Corolli and Guglielmo Lulli and Lewis Ntaimo and Saravanan Venkatachalam",
year = "2017",
month = jan,
doi = "10.1093/imaman/dpv017",
language = "English",
volume = "28",
pages = "19--40",
journal = "IMA Journal of Management Mathematics",
issn = "1471-678X",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - A two-stage stochastic integer programming model for air traffic flow management

AU - Corolli, Luca

AU - Lulli, Guglielmo

AU - Ntaimo, Lewis

AU - Venkatachalam, Saravanan

PY - 2017/1

Y1 - 2017/1

N2 - The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting.To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines.

AB - The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting.To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines.

KW - air traffic flow management

KW - stochastic integer programming

KW - uncertainty

KW - progressivebinary heuristic

U2 - 10.1093/imaman/dpv017

DO - 10.1093/imaman/dpv017

M3 - Journal article

VL - 28

SP - 19

EP - 40

JO - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

SN - 1471-678X

IS - 1

ER -