Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-26T10:13:50.918Z Has data issue: false hasContentIssue false

Aircraft sequencing and scheduling in TMAs under wind direction uncertainties

Published online by Cambridge University Press:  13 August 2020

R.K. Cecen*
Affiliation:
Alumnus, Anadolu University, Eskisehir, Turkey
C. Cetek
Affiliation:
Eskisehir Technical University, Eskisehir, Turkey
O. Kaya
Affiliation:
Eskisehir Technical University, Eskisehir, Turkey

Abstract

Aircraft sequencing and scheduling within terminal airspaces has become more complicated due to increased air traffic demand and airspace complexity. A stochastic mixed-integer linear programming model is proposed to handle aircraft sequencing and scheduling problems using the simulated annealing algorithm. The proposed model allows for proper aircraft sequencing considering wind direction uncertainties, which are critical in the decision-making process. The proposed model aims to minimise total aircraft delay for a runway airport serving mixed operations. To test the stochastic model, an appropriate number of scenarios were generated for different air traffic demand rates. The results indicate that the stochastic model reduces the total aircraft delay considerably when compared with the deterministic approach.

Type
Research Article
Copyright
© The Author(s), 2020. 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.)

References

REFERENCES

Bennell, J.A., Mesgarpour, M. and Potts, C.N. Airport runway scheduling, Q. J. Oper. Res., 2011, 9, (2), pp 115138.CrossRefGoogle Scholar
Bianco, L., Dell’Olmo, P. and Giordani, S. Scheduling models for air traffic control in terminal areas, J. Sched., 2006, 9, (3), pp 223253.CrossRefGoogle Scholar
Atkin, J.A., Burke, E.K., Greenwood, J.S. and Reeson, D. Hybrid metaheuristics to aid runway scheduling at London Heathrow airport, Transp. Sci., 2007, 41, (1), pp 90106.CrossRefGoogle Scholar
D’ariano, A., Pistelli, M. and Pacciarelli, D. Aircraft retiming and rerouting in vicinity of airports, IET Intell. Transp. Syst., 2012, 6, (4), pp 433443.CrossRefGoogle Scholar
Furini, F., Kidd, M.P., Persiani, C.A. and Toth, P. Improved rolling horizon approaches to the aircraft sequencing problem, J. Sched., 2015, 18, (5), pp 435447.CrossRefGoogle Scholar
Samà, M. D’ariano, A., D’ariano, P. and Pacciarelli, D. Optimal aircraft scheduling and routing at a terminal control area during disturbances, Transp. Res. C Emerg. Technol., 2014, 47, (1), pp 6185.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, J. Air Transp. Manage., 2013, 32, pp 3948.CrossRefGoogle Scholar
Salehipour, A., Moslemi, N.L. and Kazemipoor, H. Scheduling aircraft landings by applying a variable neighborhood descent algorithm: Runway-dependent landing time case, J. Appl. Oper. Res., 2009, 1, (1), pp 3949.Google Scholar
Liang, M., Delahaye, D. and Marechal, P. Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system. Transp. Res. C Emerg. Technol., 2018, 95, pp 207227.CrossRefGoogle Scholar
Salehipour, A., Modarres, M. and Naeni, L.M. An efficient hybrid meta-heuristic for aircraft landing problem, Comput. Oper. Res., 2013, 40, (1), pp 207213.CrossRefGoogle Scholar
Jiang, Y., Xu, Z., Xu, X., Liao, Z. and Luo, Y. A schedule optimization model on multirunway based on ant colony algorithm, Math. Prob. Eng., 2014, 2014, pp 1–11.CrossRefGoogle Scholar
Zhan, Z.H., Zhang, J., Li, Y., Liu, O., Kwok, S.K., Ip, W.H. and Kaynak, O. An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem. IEEE Trans. Intell. Transp. Syst., 2010, 11, (2), pp 399412.Google Scholar
Beasley, J.E., Sonander, J. and Havelock, P. Scheduling aircraft landings at London Heathrow using a population heuristic, J. Oper. Res. Soc., 2001, 52, (5), pp 483493.CrossRefGoogle Scholar
Hu, X.B. and Chen, W.H. Genetic algorithm based on receding horizon control for arrival sequencing and scheduling, Eng. Appl. Artif. Intell., 2005, 18, (5), pp 633642.CrossRefGoogle Scholar
Hu, X.B. and Di Paolo, E. Binary-representation-based genetic algorithm for aircraft arrival sequencing and scheduling, IEEE Trans. Intell. Transp. Syst., 2008, 9, (2), pp 301310.Google Scholar
Hu, X.B. and Di Paolo, E. An efficient genetic algorithm with uniform crossover for air traffic control. Comput. Oper. Res., 2009, 36, (1), pp 245259.CrossRefGoogle Scholar
Pinol, H. and Beasley, J.E. Scatter search and bionomic algorithms for the aircraft landing problem, Eur. J. Oper. Res., 2006, 171, (2), pp 439462.CrossRefGoogle Scholar
Hong, Y., Cho, N., Kim, Y. and Choi, B. Multiobjective optimization for aircraft arrival sequencing and scheduling, J. Air Transp., 2017, 25, (4), pp 115122.CrossRefGoogle Scholar
Solveling, G., Solak, S., Clarke, J.P. and Johnson, E. Runway operations optimization in the presence of uncertainties, J. Guid. Control Dyn., 2011, 34, (5), pp 13731382.CrossRefGoogle Scholar
Sölveling, G. and Clarke, J.P. Scheduling of airport runway operations using stochastic branch and bound methods. Transp. Res. C Emerg. Technol., 2014, 45, pp 119137.CrossRefGoogle Scholar
Choi, S., Mulfinger, D.G., Robinson, J.E. and Capozzi, B.J. Design of an optimal route structure using heuristics-based stochastic schedulers. J. Aircr., 2015, 52, (3), pp 764777.CrossRefGoogle Scholar
Bosson, C.S. and Sun, D. Optimization of airport surface operations under uncertainty. J. Air Transp., 2016, 24, (3), pp 8492.CrossRefGoogle Scholar
Heidt, A., Helmke, H., Kapolke, M., Liers, F. and Martin, A. Robust runway scheduling under uncertain conditions, J. Air Transp. Manage., 2016, 56, (A), pp 2837.CrossRefGoogle Scholar
Ng, K.K.H., Lee, C.K.M., Chan, F.T. and Qin, Y. Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach. Transp. Res. E Logist. Transp. Rev., 2017, 106, pp 115136.CrossRefGoogle Scholar
Solak, S., Sölveling, G. Clarke, J.P.B. and Johnson, E.L. Stochastic runway scheduling, Transp. Sci., 2018, 52, (4), pp 917940.CrossRefGoogle Scholar
Liu, M., Liang, B., Zheng, F., Chu, C. and Chu, F. A two-stage stochastic programming approach for aircraft landing problem, 2018 15th International Conference on Service Systems and Service Management, Hangzhou, China, 2018, pp 16.CrossRefGoogle Scholar
Khassiba, A., Bastin, F., Gendron, B., Cafieri, S. and Mongeau, M. Extended aircraft arrival management under uncertainty: a computational study, J. Air Transp., 2019, 27, (3), pp 131143.CrossRefGoogle Scholar
Hong, Y., Choi, B. and Kim, Y. Two-stage stochastic programming based on particle swarm optimization for aircraft sequencing and scheduling. IEEE Trans. Intell. Transp. Syst., 2018, 20, (4), pp 13651377.CrossRefGoogle Scholar
Favennec, B., Rognin, L., Trzmiel, A., Vergne, F. and Zeghal, K. Point Merge in Extended Terminal Area (PMS-TE 2009-2010). EUROCONTROL Experimental Center, EEC Technical/Scientific Rept. 2010–011, Brétigny-sur-Orge Cedex, France, 2010.Google Scholar
Boursier, L., Favennec, B., Hoffman, E., Trzmiel, A., Vergne, F. and Zeghal, K. Merging arrival flows without heading instructions. In 7th USA/Europe air traffic management R&D seminar, Barcelona, Spain, 2007, pp 1–8.Google Scholar
Ivanescu, D., Shaw, C., Tamvaclis, C. and Kettunen, T. Models of air traffic merging techniques: evaluating performance of point merge. In 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) and Aircraft Noise and Emissions Reduction Symposium, South Carolina, 2009, 7013–7023.CrossRefGoogle Scholar
Sahin, O., Usanmaz, O. and Turgut, E. An assessment of flight efficiency based on the point merge system at metroplex airports, Aircr. Eng. Aerosp. Technol., 2018, 90, (1), pp 110.CrossRefGoogle Scholar
DHMI, AIP Turkey Aerodromes, General Directorate of State Airports Authority, Ankara, Turkey, 2019.Google Scholar
Al-Qahtanii, K.Y. and Elkamel, A. Planning and integration of refinery and petrochemical operations, John Wiley & Sons, 2011, Weinheim, Germany.Google Scholar
Kaya, O., Bagci, F. and Turkay, M. Planning of capacity, production, and inventory decisions in a generic reverse supply chain under uncertain demand and returns. Int. J. Prod. Res., 2014, 52, (1), pp 270282.CrossRefGoogle Scholar
Dantzig, G.B. Linear programming under uncertainty. Manage. Sci., 1955, 1, pp 197206.CrossRefGoogle Scholar
Birge, J.R. and Louveaux, F. Introduction to Stochastic Programming, Springer Series in Operations Research, Springer Verlag, 1997, New York.Google Scholar
Kall, P. and Wallace, S.W. Stochastic Programming, John Wiley, 1994, Chichester.Google Scholar
Turgut, E.T. and Usanmaz, Ö. An analysis of vertical profiles of wind and humidity based on long-term radiosonde data in Turkey, Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A-Uygulamalı Bilimler ve Mühendislik, 2016, 17, (5), pp 830844.Google Scholar