Hostname: page-component-6766d58669-bkrcr Total loading time: 0 Render date: 2026-05-17T10:46:51.545Z Has data issue: false hasContentIssue false
  • English
  • Français

Runway assignment optimisation model for Istanbul Airport considering multiple parallel runway operations

Published online by Cambridge University Press:  16 September 2024

A. Güven Open the ORCID record for A. Güven [Opens in a new window] ,
F. Aybek Cetek Open the ORCID record for F. Aybek Cetek [Opens in a new window]  and
R.K. Cecen Open the ORCID record for R.K. Cecen [Opens in a new window]
A. Güven
Affiliation:
General Directorate of State Airports Authority (DHMI), Ankara, Turkey
F. Aybek Cetek*
Affiliation:
Eskişehir Technical University, Eskişehir, Turkey
R.K. Cecen
Affiliation:
Eskişehir Osmangazi University, Eskişehir, Turkiye
*
Corresponding author: F. Aybek Cetek; Email: faybek@eskisehir.edu.tr
Get access Rights & Permissions [Opens in a new window]

Abstract

The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data.

Keywords

runway assignment optimisationmultiple parallel runwaystaxi operationmixed integer linear programmingair traffic management

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1330 , December 2024 , pp. 2799 - 2814
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

EUROCONTROL. European Avıatıon in 2040 Challenges Of Growth Annex1 Flight Forecast to 2040, Bruksel, 2018.Google Scholar
Delsen, J.G. Flexible Arrival & Departure Runway Allocation Using Mixed-Integer Linear Programming: A Schiphol Airport Case Study, 2016.Google Scholar
Idris, H., et al. Identification of flow constraint and control points in departure operations at airport systems, Guidance, Navigation, and Control Conference and Exhibit, 1998.Google Scholar
Airports Council International and International Air Transport Association. Airpon CapacityDemand Management, 3rd ed, Airports Council International: International Air Transport Association, 1996, Geneva. Google Scholar
Bazargan, M., Fleming, K. and Subramanian, P. A simulation study to investigate runway capacity using TAAM, Proceedings of the Winter Simulation Conference, IEEE, 2002, pp 1235–1243.CrossRefGoogle Scholar
Jiang, Y., Liu, Z., Hu, Z., Zhang, H. and Xu, C. Variable neighbourhood search for the integrated runway sequencing, taxiway scheduling, and gate reassignment problem, Transp. B Transport Dyn., 2023, 11, (1), pp 744–759.Google Scholar
https://www.easa.europa.eu/en/domains/environment/icao-aircraft-engine-emissions-databank (access date: 20.12.2022).Google Scholar
Ikli, S., Mancel, C., Mongeau, M., Olive, X. and Rachelson, E. The aircraft runway scheduling problem: A survey, Comput. Operat. Res., 2021, 132, 105336.CrossRefGoogle Scholar
Hong, Y., Cho, N., Kim, Y. and Choi, B. Multi-objective optimisation for aircraft arrival sequencing and scheduling, J. Air Transp., 2017, 25, (4), pp 115–122.CrossRefGoogle Scholar
Cecen, R.K. and Kursat, R. Multi-objective TMA management optimisation using the point merge system, Aircraft Eng. Aerospace Technol., 2021, 93, (1), pp 15–24.CrossRefGoogle Scholar
Cecen, R.K., Saraç, T. and Çetek, C. (2022). Emission and flight time optimisation model for aircraft landing problem, Transp. Res. Rec., 2023, 2677, (2), pp 763–773.CrossRefGoogle Scholar
Ma, J., Delahaye, D., Sbihi, M., Scala, P. and Mujica Mota, M.A. Integrated optimisation of terminal maneuvering area and airport at the macroscopic level, Transp. Res. Part C Emerging Technol., 2019, 98, pp 338–357.CrossRefGoogle Scholar
Cecen, R.K., Cetek, C. and Kaya, O. Aircraft sequencing and scheduling in TMAs under wind direction uncertainties, Aeronaut. J., 2020, 124, (1282), pp 1896–191.CrossRefGoogle Scholar
Ghoniem, A., Sherali, H.D. and Baik, H. Enhanced models for a mixed arrival-departure aircraft sequencing problem, INFORMS J. Comput., 2014, 26, (3), pp 514–530.CrossRefGoogle Scholar
Cecen, R.K. A path stretching model for effective terminal airspace management, Int. J. Aeronaut. Space Sci., 2022, 23, (5), pp 1043–1052.CrossRefGoogle Scholar
Liu, W., Delahaye, D., Cetek, F.A., Zhao, Q. and Notry, P. Comparison of performance between PMS and trombone arrival route topologies in terminal maneuvering area, J. Air Transport Manag., 2024, 115, p 102532. CrossRefGoogle Scholar
Salehipour, A. An algorithm for single-and multiple-runway aircraft landing problem, Math. Comput. Simul., 2020, 175, pp 179–191.CrossRefGoogle Scholar
Dönmez, K., Çetek, C. and Kaya, O. Aircraft sequencing and scheduling in parallel-point merge systems for multiple parallel runways, Transp. Res. Rec., 2022, 2676, (3), pp 108–124.CrossRefGoogle Scholar
Dönmez, K., Çetek, C. and Kaya, O. Air traffic management in parallel-point merge systems under wind uncertainties, J. Air Transport Manag., 2022, 104, p 102268.CrossRefGoogle Scholar
Guépet, J., Briant, O., Gayon, J. and Acuna-Agost, R. ntegration of aircraft ground movements and runway operations, Transp. Res. Part E Logist. Transp. Rev., 2017, 104, pp 131–149.CrossRefGoogle Scholar
Rodríguez-Sanz, Á., Arnaldo Valdes, R.M.M., Pérez-Castán, J.A., López Cózar, P. and Comendador, V.F.G. Tactical runway scheduling for demand and delay management, Aircraft Eng. Aerospace Technol., 2021, 94, (1), pp 2–13.CrossRefGoogle Scholar
Sölveling, G., Solak, S., Clarke, J.B. and Johnson, E.L. Scheduling of runway operations for reduced environmental impact, Transp. Res. Part D Transport Environ., 2011, 16, (2), pp 110–120.CrossRefGoogle Scholar
Lieder, A. and Stolletz, R. Scheduling aircraft take-offs and landings on interdependent and heterogeneous runways, Transp. Res. Part E Logist. Transp. Rev., 2016, 88, pp 167–188.CrossRefGoogle Scholar
Ng, K.K.H., Lee, C.K.M., Zhang, S.Z. and Keung, K.L. The impact of heterogeneous arrival and departure rates of flights on runway configuration optimisation, Transp. Lett., 2022, 14, (3), pp 215–226.CrossRefGoogle Scholar
Cecen, R.K. Fuel-optimal aircraft arrival operations in extended terminal maneuvering areas, Transp. Res. Rec., 2022, 2676, (6), pp 330–339.CrossRefGoogle Scholar
Weiszer, M., Chen, J., Stewart, P. and Zhang, X. Preference-based evolutionary algorithm for airport surface operations, Transp. Res. Part C Emerging Technol., 2018, 91, pp 296–316.CrossRefGoogle Scholar
Fritzsche, M., Günther, T. and Fricke, H. Potential of dynamic aircraft to runway allocation for parallel runways, 4th International Conference on Research in Air Transportation, 2010.Google Scholar
https://www.igairport.aero/tr/iga-hakkinda/zaman-yolculugu (access date: 02.10.2022).Google Scholar
http://iga.phtools.net/insaat-asamalar.html (access date: 02.10.2022).Google Scholar
Air Navigation service provider and state airports authority of Türkiye, Annual Report 2021, 2022, Ankara, Türkiye.Google Scholar
Güven, A. and Çetek, F.A. İstanbul Havalimanı’nın Çoklu Paralel Pist Konfigürasyonlarının Zaman ve Yakıt Tüketimi Açısından İncelenmesi, Trafik ve Ulaşım Araştırmaları Dergisi, 2022, 5, ( 2), pp 130–141.CrossRefGoogle Scholar
General Directorate of State Airports Authority. Aeronautical Information Publication (AIP), 2022, Ankara, Türkiye.Google Scholar
ICAO, Doc. 4444 AIr Traffic Management. Doc 4444 AIr Traffic Management, 2016.Google Scholar