Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-23T13:13:48.069Z Has data issue: false hasContentIssue false
  • English
  • Français

A Mathematical Programming Approach to Optimum Airspace Sectorisation Problem

Published online by Cambridge University Press:  11 November 2019

Hakan Oktal ,
Kadriye Yaman  and
Refail Kasımbeyli
Hakan Oktal*
Affiliation:
(Faculty of Aeronautics and Astronautics, Eskisehir Technical University, Eskisehir, Turkey)
Kadriye Yaman
Affiliation:
(Faculty of Aeronautics and Astronautics, Eskisehir Technical University, Eskisehir, Turkey)
Refail Kasımbeyli
Affiliation:
(Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkey)
*
(E-mail: hoktal@eskisehir.edu.tr)
Get access Rights & Permissions [Opens in a new window]

Abstract

The aim of this study is to provide a balanced distribution of air traffic controller workload (ATCW) across airspace sectors taking into account the complexity of airspace sectors and the factors affecting ATCW, both objective and perceived. Almost all the studies focusing on the airspace sectorisation problem use heuristic or metaheuristic algorithms in dynamic simulation environments instead of a mathematical modelling approach. The paper proposes a multi-objective mixed integer mathematical model for airspace sectorisation. The model is applied to the upper, en-route level of Turkish airspace. Geographical information systems (GIS) are used to advantage for airspace analysis. The multi-objective model developed in this paper is scalarised by using the conic scalarisation method. For solving the scalarised problem, the CPLEX and DICOPT solvers of GAMS software are implemented. Finally, the optimal sector boundaries of Turkish airspace are defined.

Keywords

Air TransportationAirspace SectorisationAir Traffic Controller WorkloadMultiple Objective ProgrammingConic Scalarisation
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 3 , May 2020 , pp. 599 - 612
Copyright
Copyright © The Royal Institute of Navigation 2019

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

Basu, A., Mitchell, J. S. B. and Sabhnani, G. K. (2009). Geometric algorithms for optimal airspace design and air traffic controller workload balancing. Journal of Experimental Algorithmics, 14, 24.CrossRefGoogle Scholar
Cao, X., Zhu, X., Tian, Z., Chen, J., Wu, D. and Du, W. (2018). A knowledge-transfer-based learning framework for airspace operation complexity evaluation. Transportation Research Part C, 95, 61–81.CrossRefGoogle Scholar
Delahaye, D., Schoenauer, M. and Alliot, J. M. (1998). Airspace Sectoring by Evolutionary Computation. Proceedings of the IEEE World Congress on Computational Intelligence, Anchorage, Alaska, USA, 218–223.CrossRefGoogle Scholar
DHMI. (2008). Turkish AIP-Aeronautical Information Publication. Ankara: Aeronautical Publication Service.Google Scholar
Djokic, J., Lorenz, B. and Fricke, H. (2010). Air traffic control complexity as workload driver. Transportation Research Part C, 18, 930–936.CrossRefGoogle Scholar
Eiselt, H. A. and Sandblom, C. L. (2000). Chapter 7: Heuristic Algorithms. Integer Programming and Network Models. Springer, Berlin-Heidelberg, 229–258.CrossRefGoogle Scholar
Eurocontrol. (2003). Eurocontrol Manual for Airspace Planning. Vol. 2, Edition 2.0, Brussels.Google Scholar
Gasimov, R. N. (2001). Characterization of the Benson proper efficiency and scalarization in nonconvex vector optimization. Multiple Criteria Decision Making in the New Millenium, Lecture Notes in Economics and Mathematical Systems, Springer, 507. 189–198.Google Scholar
Gerdes, I., Temme, A. and Schultz, M. (2018). Dynamic airspace sectorisation for flight-centric operations. Transportation Research Part C, 95, 460–480.CrossRefGoogle Scholar
ICAO. (2007). Procedures for Air Navigation Services: Air Traffic Management (Doc.4444 ATM/501), International Civil Aviation Organization, Montreal, Canada.Google Scholar
Kasimbeyli, R. (2010). A nonlinear cone separation theorem and scalarization in nonconvex vector optimization. Society for Industrial and Applied Mathematics-SIAM Journal of Optimisation, 20(3), 1591–1619.Google Scholar
Kasimbeyli, R., Ozturk, Z. K., Kasimbeyli, N., Yalcin, G. D. and Erdem, B. I. (2019). Comparison of some scalarization methods in multiobjective optimization, 42(5), 1875–1905.Google Scholar
Klein, A. (2005). An Efficient Method for Airspace Analysis and Partitioning Based on Equalized Traffic Mass. Proceedings of the 6th USA/Europe ATM Seminar, Baltimore, MD, USA.Google Scholar
Laudeman, I. V., Shelden, S. G., Branstrom, R. and Brasil, C. L. (1998). Dynamic Density: An Air Traffic Management Metric. NASA/TM: 1998-112226, National Aeronautics and Space Administration, California.Google Scholar
Lindberg, L. G. V. and Värbrand, P. (2001). Absolute Flow Control: Final Report. https://www.eurocontrol.int/eec/gallery/content/public/documents/projects/CARE/linkoping-avtech-finalreport.pdf. [Accessed 15 Dec. 2018].Google Scholar
Majumdar, A., Ochieng, W. and Polak, J. (2002). Estimation of European airspace capacity from a model of controller workload. The Journal of Navigation, 55, 381–403.CrossRefGoogle Scholar
Martinez, S. A., Chatterji, G. B., Sun, D. and Bayen, A. M. (2007). A Weighted-Graph Approach for Dynamic Airspace Configuration. Proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit, South Carolina.CrossRefGoogle Scholar
Nuic, A. (2004). Aircraft Performance Summary Tables for The Base of Aircraft Data (BADA). ECC Note No.12/04, Revision 3.6, Eurocontrol Experimental Centre, Brétigny-sur-Orge, France.Google Scholar
Oktal, H. and Yaman, K. (2011). A new approach to air traffic controller workload measurement and modeling. Aircraft Engineering and Aerospace Technology, 83(1), 35–42.CrossRefGoogle Scholar
Rahman, S. M. B. A., Sidik, M. F., Nazir, F. N. and Jamil, M. S. A. (2018). Air traffic controller perception towards air traffic and taskload. International Journal of Engineering & Technology, 7(4.25), 154–159.Google Scholar
Sergeeva, M., Delahaye, D., Mancel, C. and Vidosavljevic, A. (2017). Dynamic airspace configuration by genetic algorithm. Journal of Traffic and Transportation Engineering, 4(3), 300–314.Google Scholar
Tobaruela, G., Schuster, W., Majumdar, A., Ochieng, W. Y., Martinez, L. and Hendrickx, P. (2014). A method to estimate air traffic controller mental workload based on traffic clearances. Journal of Air Transport Management, 39, 59–71.CrossRefGoogle Scholar
Trandac, H., Baptiste, P. and Duong, V. (2003). Optimized Sectorization of Airspace with Constraints. Proceedings of the 5th USA/Europe ATM R&D Seminar, Budapest, Hungary.Google Scholar
Wang, Y., Vormer, F., Hu, M. and Duong, V. (2013). Empirical analysis of air traffic controller dynamics. Transportation Research Part C, 33, 203–213.CrossRefGoogle Scholar
Yangzhou, C. and Defu, Z. (2014). Dynamic airspace configuration method based on a weighted graph model. Chinese Journal of Aeronautics, 27(4), 903–912.Google Scholar
Yousefi, A. (2005). Optimum airspace design with air traffic controller workload-based partitioning. PhD thesis, George Mason University, Fairfax, Virginia.Google Scholar
Zhu, X., Cao, X. and Cai, K. (2017). Measuring air traffic complexity based on small samples. Chinese Journal of Aeronautics, 30(4), 1493–1505.CrossRefGoogle Scholar