Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-29T17:13:50.410Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimising aircraft arrivals in terminal airspace by mixed integer linear programming model

Published online by Cambridge University Press:  21 February 2020

R.K. Cecen Open the ORCID record for R.K. Cecen [Opens in a new window]  and
F. Aybek Çetek
R.K. Cecen*
Affiliation:
Alumnus Anadolu University, Eskisehir, Turkey
F. Aybek Çetek*
Affiliation:
Assistant Professor Eskisehir Technical University, Eskisehir, Turkey
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Air traffic flow becomes denser and more complex within terminal manoeuvering areas (TMAs) due to rapid growth rates in demand. Effective TMA arrival management plays a key role in the improvement of airspace capacity, flight efficiency and air traffic controller performance. This study proposes a mixed integer linear programming model for aircraft landing problems with area navigation (RNAV) route structure using three conflict resolution and sequencing techniques together: flexible route allocation, airspeed reduction and vector manoeuver. A two-step mixed integer linear programming model was developed that minimises total conflict resolution time and then total airborne delay using lexicographic goal programming. Experimental results demonstrate that the model can obtain conflict-free and time optimal aircraft trajectories for RNAV route structures.

Keywords

Air traffic flow managementconflict-free arrival sequencingmixed integer linear programminggoal programmingextended terminal airspace
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1278 , August 2020 , pp. 1129 - 1145
Copyright
© Royal Aeronautical Society 2020

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.)

References

REFERENCES

IATA, 2019: https://www.iata.org/pressroom/facts_figures/fact_sheets/Documents/fact-sheet-industry-facts.pdf, Accessed: 27.10.2019.Google Scholar
Aybek Cetek, F. “TMA simulation model proposal for dynamic gate assignment in terminal control areas”. Diss. PH. D. dissertation, Department of Air Traffic Control, Anadolu University, 2015.Google Scholar
Eurocontrol. RNAV Application in Terminal Airspace – An ATC Operational Perspective, 2nd edition 1999.Google Scholar
Adler, N., Liebert, V., Yazhemsky, E.Benchmarking airports from a managerial Perspective, Omega, 2013, 41, (2), 442–458.CrossRefGoogle Scholar
Bennell, J.A., Mesgarpour, M., Potts, C.N.Airport runway scheduling, 4OR- Quarterly Journal of Operations Research, 2011, 9, (2), 115–138.CrossRefGoogle Scholar
Samà, M., D’ariano, A., D’ariano, P. and Pacciarelli, D.Scheduling models for optimal aircraft traffic control at busy airports: tardiness, priorities, equity and violations considerations, Omega, 2017, 67, 81–98.CrossRefGoogle Scholar
Ernst, A.T., Krishnamoorthy, M. and Storer, R.H.Heuristic and exact algorithms for scheduling aircraft landings, Networks, 1999, 34, (3), 229–241.3.0.CO;2-W>CrossRefGoogle Scholar
Beasley, J.E., Krishnamoorthy, M., Sharaiha, Y.M. and Abramson, D. “Displacement problem and dynamically scheduling aircraft landings”, Journal of the Operational Research Society, 2004, 55, (1), 54–64.CrossRefGoogle Scholar
Hu, X. and Chen, W.H. “Receding horizon control for aircraft arrival sequencing and scheduling.” IEEE Transactions on Intelligent Transportation Systems, 2005, 6, (2), 189–197.CrossRefGoogle Scholar
Bianco, L., Dell’olmo, P. and Giordani, S.Scheduling models for air traffic control in terminal areas, Journal of Scheduling, 2006, 9, (3), 180–197.CrossRefGoogle Scholar
Soomer, M.J. and Franx, G.J.Scheduling aircraft landings using airlines’ preferences, European Journal of Operations Research, 2008, 190, (1), 277–291.CrossRefGoogle Scholar
Artiouchine, K., Baptiste, P. and Dürr, C.Runway sequencing with holding patterns, European Journal of Operational Research, 2008, 189, (3), 1254–1266.CrossRefGoogle Scholar
Hu, X. and Paolo, D.E.An efficient genetic algorithm with uniform crossover for air traffic control, Computers and Operations Research, 2009, 36, (1), 245–259.CrossRefGoogle Scholar
Eun, Y., Hwang, I. and Bang, H.Optimal arrival flight sequencing and scheduling using discrete airborne delays, IEEE Transactions on Intelligent Transportation Systems, 2010, 11, (2), 359–373.Google Scholar
Zuniga, C., Delahaye, D. and Piera, M.A. Integrating and sequencing flows in terminal maneuvering area by evolutionary algorithms. DASC 2011, 30th IEEE/AIAA Digital Avionics Systems Conference, Oct 2011, Seattle, United States. IEEE, 1–32.CrossRefGoogle Scholar
Sölveling, G., Solak, S., Clarke, J.P.B. and Johnson, E.L., Scheduling of runway operations for reduced environmental impact, Transportation Research Part D: Transport and Environment, 2011, 16, (2), 110–120.CrossRefGoogle Scholar
Hancerliogullari, G., Rabadi, G., Al-Salem, A.H. and Kharbeche, M.Greedy algorithms and metaheuristics for a multiple runway combined arrival-departure aircraft sequencing problem, Journal of Air Transport Management, 2013, 32, 39–48.CrossRefGoogle Scholar
Samà, M., D’ariano, A. and Pacciarelli, D.Rolling horizon approach for aircraft scheduling in the terminal control area of busy airports, Procedia-Social and Behavioral Sciences, 2013, 80, 531–552.CrossRefGoogle Scholar
Sabar, Nasser R., and Kendall, G.An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem, Omega, 2015, 56, 88–98.CrossRefGoogle Scholar
Liang, M., Delahaye, D. and Maréchal, P.Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities, Transportation Research Part C: Emerging Technologies, 2017, 85, 268–291.CrossRefGoogle Scholar
Liang, M., Delahaye, D. and Maréchal, P.Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system, Transportation Research Part C: Emerging Technologies, 2018, 95, 207–227.CrossRefGoogle Scholar
Erkan, H., Erkip, N.K. and Şafak, ö.Collaborative decision making for air traffic management: a generic mathematical program for the rescheduling problem, Computers & Industrial Engineering, 2019, 137, 106016.CrossRefGoogle Scholar
Samà, M., D’ariano, A., Palagachev, K. and Gerdts, M.Integration methods for aircraft scheduling and trajectory optimization at a busy terminal maneuvering area, OR Spectrum, 2019, 41, (3), 641–681.CrossRefGoogle Scholar
Sáez, R., Prats, X., Polishchuk, T., Polishchuk, V. and Schmidt, C. Automation for separation with CDOs: dynamic aircraft arrival routes In ATM Seminar, Vienna, Austria, 2019.CrossRefGoogle Scholar
Dahlberg, J., Granberg, T.A., Polishchuk, T., Schmidt, C. and Sedov, L. Capacity-driven automatic design of dynamic aircraft arrival routes. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference, London, UK, 2018, 1–9.CrossRefGoogle Scholar
Zhou, J., Cafieri, S., Delahaye, D., and Sbihi, M.Optimizing the design of a route in terminal maneuvering area using branch and bound. In Air Traffic Management and Systems II, Springer, 2017, Tokyo, 171–184.CrossRefGoogle Scholar
Bianco, L. and Bielli, M.Air traffic management: Optimization models and algorithms, Journal of Advanced Transportation. 1992, 26, (2), 131–167.CrossRefGoogle Scholar
https://www.aci-europe.org/policy/position-papers.html?view=group&group=1&id=11, [Accessed: 27.10.2019].Google Scholar
Aeronautical Information Publication, Aerodrome, Turkey, December 2017.Google Scholar
Sheehan, C. “Coverage of 2012 European Air Traffic for the Base of Aircraft Data (BADA)”, Revision 3.11, 2007.Google Scholar
ICAO, Rules of the Air and Air Traffic Services Doc 4444 ATM 501, “Air Traffic Management”, ICAO Publications, Fifteenth Edition, Montreal, 2007.Google Scholar