A metaheuristic approach for aircraft landing sequencing and to increase runway capacity
DOI:
https://doi.org/10.14295/transportes.v29i4.2500Keywords:
Aircraft landing sequencing, Air traffic flow management, Runway capacity, Optimization, MetaheuristicsAbstract
Runway capacity problems are present at several airports around the world. The efficient and effective execution of the aircraft sequencing for landing has become an alternative for increasing runway capacity at the tactical level. The aircraft sequencing problem aims to determine the best aircraft processing order for landing towards optimizing the runway usage and mitigate delays, among other objectives, subject to a series of operational restrictions. This study aims to develop a solution method for the aircraft sequencing problem that is capable of producing runway capacity gains, generating feasible solutions in low computational time and maintaining equity among the airlines by respecting the maximum number of aircraft position changes in a new sequence. The method is based on the simulated annealing metaheuristic adapted to the context of the problem studied. The Airland dataset, available in the OR-library, and actual data from the São Paulo/Guarulhos International Airport were used to evaluate the potential benefits of the method proposed. The results showed capacity gains of up to 21% for the theoretical data and of 10% for the actual data.
Downloads
References
Andreeva-Mori, A.; S. Suzuki e E. Itoh (2013) Rule derivation for arrival aircraft sequencing. Aerospace Science and Technology, n. 30, p. 200-209. DOI: 10.1016/j.ast.2013.08.004
Avella, P.; M. Boccia; C. Mannino e I. Vasilyev (2017) Time-indexed formulations for the runway scheduling problem. Trans-portation Science, n. 51, p. 1196-1209. DOI: 10.1287/trsc.2017.0750
Balakrishnan, H. e B. G. Chandran (2010) Algorithms for scheduling runway operations under constrained position shifting. Operations Research, n. 58, p. 1650-1665. DOI: 10.1287/opre.1100.0869
Barnhart, C. e B. Smith (2012) Quantitative problem-solving methods in the airline industry. Heidelberg: Springer. DOI: 10.1007/978-1-4614-1608-1
Beasley, J. E.; M. Krishnamoorthy; Y. M. Sharaiha e D. Abramson (2000) Scheduling aircraft landings—the static case. Trans-portation science, n. 34, p. 180-197. DOI: 10.1287/trsc.34.2.180.12302
Beasley, J. E.; M. Krishnamoorthy; Y. M. Sharaiha e D. Abramson (2004) Displacement problem and dynamically scheduling aircraft landings. Journal of the operational research society, n. 55, p. 54-64. DOI: 10.1057/palgrave.jors.2601650
Bennell, J. A.; M. Mesgarpour e C. N. Potts (2011) Airport runway scheduling. 4OR, n. 9, p. 115. DOI: 10.1007/s10288-011-0172-x
Bennell, J. A.; M. Mesgarpour e C. N. Potts (2013) Airport runway scheduling. Annals of Operations Research, n. 204, p. 249-270. DOI: 10.1007/s10479-012-1268-1
Bennell, J. A.; M. Mesgarpour e C. N. Potts (2016) Dynamic scheduling of aircraft landings. European Journal of Operational Research, n. 258, p. 315-327. DOI: 10.1016/j.ejor.2016.08.015
Briskorn, D. e R. Stolletz (2013) Aircraft landing problems with aircraft classes. Journal of Scheduling, n. 17, p. 31-45. DOI: 10.1007/s10951-013-0337-x
Brueckner, J. K. (2002) Internalization of airport congestion. Journal of Air Transport Management, n. 8, p. 141-147. DOI: 10.1016/s0969-6997(01)00049-7
Caccavale, M. V.; A. Iovanella; C. Lancia; G. Lulli e B. Scoppola (2014) A model of inbound air traffic: The application to Heathrow airport. Journal of Air Transport Management, n. 34, p. 116-122. DOI: 10.1016/j.jairtraman.2013.09.004
Capri, S. e M. Ignaccolo (2004) Genetic algorithms for solving the aircraft-sequencing problem: the introduction of depar-tures into the dynamic model. Journal of Air Transport Management, n. 10, p. 345-351. DOI: 10.1016/j.jairtraman.2004.05.004
Fahle, T.; R. Feldmann; S. Götz; S. Grothklags e B. Monien (2004) The aircraft sequencing problem. Computer science in per-spective. Springer. DOI: 10.1007/3-540-36477-3_11
Faye, A. (2015) Solving the aircraft landing problem with time discretization approach. European Journal of Operational Re-search, n. 242, p. 1028-1038. DOI: 10.1016/j.ejor.2014.10.064
Furini, F.; M. P. Kidd; C. A. Persiani e P. Toth (2015) Improved rolling horizon approaches to the aircraft sequencing problem. Journal of Scheduling, n. 18, p. 435-447. DOI: 10.1007/s10951-014-0415-8
Ghoniem, A. e F. Farhadi (2015) A column generation approach for aircraft sequencing problems: a computational study. Journal of the Operational Research Society, n. 66, p. 1717-1729. DOI: 10.1057/jors.2014.131
Ghoniem, A.; F. Farhadi e M. Reihaneh (2015) An accelerated branch-and-price algorithm for multiple-runway aircraft se-quencing problems. European Journal of Operational Research, n. 246, p. 34-43. DOI: 10.1016/j.ejor.2015.04.019
Ghoniem, A.; H. D. Sherali e H. Baik (2014) Enhanced models for a mixed arrival-departure aircraft sequencing problem. INFORMS Journal on Computing, n. 26, p. 514-530. DOI: 10.1287/ijoc.2013.0581
Gillen, D.; A. Jacquillat e A. R. Odoni (2016) Airport demand management: The operations research and economics perspec-tives and potential synergies. Transportation Research Part A: Policy and Practice, n. 94, p. 495-513. DOI: 10.1016/j.tra.2016.10.011
Harikiopoulo, D. e N. Neogi (2011) Polynomial-time feasibility condition for multiclass aircraft sequencing on a single-runway airport. IEEE transactions on intelligent transportation systems, n. 12, p. 2-14. DOI: 10.2514/6.2004-6547
Hirst, M. (2008) The air transport system. Amsterdam: Elsevier. DOI: 10.1533/9781845695224
Hoffman, K.L; M. Padberg e G. Rinaldi (2013) Traveling salesman problem. Encyclopedia of operations research and manage-ment science, n. 1, p. 1573-1578. DOI: 10.1007/978-1-4419-1153-7_1068
Hu, X. B. e W. H. Chen (2005) Genetic algorithm based on receding horizon control for arrival sequencing and scheduling. Engineering Applications of Artificial Intelligence, n. 18, p. 633-642. DOI: 10.1016/j.engappai.2004.11.012
Hu, X. B. e E. A. D. Paolo (2011) A ripple-spreading genetic algorithm for the aircraft sequencing problem. Evolutionary Com-putation, n. 19, p. 77-106. DOI: 10.1162/evco_a_00011
ICAO (2014) Doc 9971 Manual on Collaborative Air Traffic Flow Management. International Civil Aviation Organization
Jacquillat, A. e A. R. Odoni (2015) Endogenous control of service rates in stochastic and dynamic queuing models of airport congestion. Transportation Research Part E: Logistics and Transportation Review, n. 73, p. 133-151. DOI: 10.1016/j.tre.2014.10.014
Ji, X. P.; X. B. Cao; W. B. Du e K. Tang (2016) An evolutionary approach for dynamic single-runway arrival sequencing and scheduling problem. Soft Computing, n. 21, p. 7021-7037. DOI: 10.1007/s00500-016-2241-8
Lieder, A. e R. Stolletz (2016) Scheduling aircraft take-offs and landings on interdependent and heterogeneous runways. Transportation research part E: logistics and transportation review, n. 88, p. 167-188. DOI: 10.2139/ssrn.2657577
Liu, Y. H. (2010) A genetic local search algorithm with a threshold accepting mechanism for solving the runway dependent aircraft landing problem. Optimization Letters, n. 5, p. 229-245. DOI: 10.1007/s11590-010-0203-0
Luenberger, R. A. (1988) A traveling-salesman-based approach to aircraft scheduling in the terminal area. Houston: National Aeronautics and Space Administration.
Ma, W.; B. Xu; M. Liu e H. Huang (2014) An efficient approximation algorithm for aircraft arrival sequencing and scheduling problem. Mathematical Problems in Engineering. DOI: 10.1155/2014/236756
Madas, M. A. e K. G. Zografos (2008) Airport capacity vs. demand: mismatch or mismanagement? Transportation Research Part A: Policy and Practice, n. 42, p. 203-226. DOI: 10.1016/j.tra.2007.08.002
Nolan, M. (2011) Fundamentals of air traffic control. Boston: Cengage learning.
Pamplona, D. A.; T. Hermeto; C. J. P. Alves; C. Muller (2015) Impactos na capacidade aérea através da recategorização da esteira de turbulência das aeronaves. Revista de Engenharia e Tecnologia, n. 7, p. 162-175.
Rodríguez-Díaz, A.; B. Adenso-Díaz e P. L. González-Torre (2017) Minimizing deviation from scheduled times in a single mi-xed-operation runway. Computers & Operations Research, n. 78, p. 193-202. DOI: 10.1016/j.cor.2016.09.014
Sama, M.; A. D’ariano; P. D’ariano e D. Pacciarelli (2016) Scheduling models for optimal aircraft traffic control at busy air-ports: tardiness, priorities, equity and violations considerations. Omega, n. 67, p. 81-98. DOI: 10.1016/j.omega.2016.04.003
Skorupski, J. e A. Florowski (2016) Method for evaluating the landing aircraft sequence under disturbed conditions with the use of Petri nets. The Aeronautical Journal, n. 120, p. 819-844. DOI: 10.1017/aer.2016.32
Sölveling, G.; S. Solak; J. P. B. Clarke e E. L. Johnson (2011) Scheduling of runway operations for reduced environmental im-pact. Transportation Research Part D: Transport and Environment, n. 16, p. 110-120. DOI: 10.1287/opre.1100.0869
Sylejmani, K.; E. Bytyçi e A. Dika (2017) Solving aircraft sequencing problem by using genetic algorithms. Intelligent Decision Technologies, p. 1-13. DOI: 10.3233/idt-170309
Tian, Y.; L. Wan; K. Han e B. Ye (2018) Optimization of terminal airspace operation with environmental considerations. Transportation Research Part D: Transport and Environment, n. 63, p. 872-889. DOI: 10.1016/j.trd.2018.06.018
TRB (2012) Evaluating Airfield Capacity - ACRP Report 79. Washington, D.C.: Transportation Research Board
Trivizas, D. A. (1998) Optimal scheduling with maximum position shift (MPS) constraints: A runway scheduling application. The Journal of Navigation, n. 51, p. 250-266. DOI: 10.1017/s0373463397007625
Tu, Y.; M. O. Ball e W. S. Jank (2008) Estimating flight departure delay distributions—a statistical approach with long-term trend and short-term pattern. Journal of the American Statistical Association, n. 103, p. 112-125. DOI: 10.2139/ssrn.923628
Vasilyev, I. L.; P. Avella e M.Boccia (2016) A branch and cut heuristic for a runway scheduling problem. Automation and Re-mote Control, n. 77, p. 1985-1993. DOI: 10.1134/s0005117916110084
Xu, B. (2017) An efficient Ant Colony algorithm based on wake-vortex modeling method for aircraft scheduling problem. Journal of Computational and Applied Mathematics, n. 317, p. 157-170. DOI: 10.1016/j.cam.2016.11.043
Zhan, Z. H.; J. Zhang; Y. Li; O. Liu; S. Kwok; W. Ip e O. Kaynak (2010) An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem. IEEE Transactions on Intelligent Transportation Sys-tems, n. 11, p. 399-412. DOI: 10.1145/1569901.1570148
Zhang, X.; X. Zhang; J Zhang e B. Liu (2007) Optimization of sequencing for aircraft arrival based on approach routes. Belle-vue: IEEE Intelligent Transportation Systems Conference, p. 592-596. DOI: 10.1109/itsc.2007.4357670
Zhou, H. e X. Jiang (2015) Multirunway optimization schedule of airport based on improved genetic algorithm by dynamical time window. Mathematical Problems in Engineering. DOI: 10.1155/2015/854372
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Daniel Alberto Pamplona, Mayara C.R Murça, Alexandre G. de Barros, Claudio Jorge Pinto Alves
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who submit papers for publication by TRANSPORTES agree to the following terms:
- Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.
- Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.
- Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.
- Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.