Optimal model for the aircraft arrival and departure scheduling problem with fuzzy runway incursion time

Bo Sun; Ming Wei; Binbin Jing; Bo Sun; Ming Wei; Binbin Jing

doi:10.3934/mbe.2021334

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6724-6738. doi: 10.3934/mbe.2021334

Previous Article Next Article

Research article Special Issues

Optimal model for the aircraft arrival and departure scheduling problem with fuzzy runway incursion time

1.
Key Laboratory of wide area surveillance and safety control technology of civil aviation flight, Civil Aviation University of China, Tianjin 300300, China
2.
School of Air traffic Management, Civil Aviation University of China, Tianjin 300300, China
3.
School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China

Academic Editor: Cheng Siong Chin

Received: 02 June 2021 Accepted: 30 July 2021 Published: 10 August 2021

This paper presents an optimization model for assigning a set of arrival and departure flights to multiple runways and determining their actual times with consideration of incursions. Due to the lack of data, fuzzy incursion time is used to describe the uncertainty with the help of artificial experience. Moreover, the multiple-goal priority considerations of air traffic controllers are also fully considered in this model. The two objectives are to simultaneously minimize delays in arrival and departure flights. Since this problem is NP-hard, a novel polynomial algorithm based on queuing theory is also proposed to obtain acceptable solutions efficiently. Finally, a real-world example is provided to analyze the effect of different times and places of incursion events on the scheduling scheme, which can verify the correctness of the model. Results show that higher runway incursion times lead to longer queue lengths for take-off and landing flights, resulting in more flight delays.
- arrival and departure flight scheduling,
- multiple runways,
- fuzzy runway incursion time,
- heuristic algorithm
Citation: Bo Sun, Ming Wei, Binbin Jing. Optimal model for the aircraft arrival and departure scheduling problem with fuzzy runway incursion time[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6724-6738. doi: 10.3934/mbe.2021334

Related Papers:

Abstract

This paper presents an optimization model for assigning a set of arrival and departure flights to multiple runways and determining their actual times with consideration of incursions. Due to the lack of data, fuzzy incursion time is used to describe the uncertainty with the help of artificial experience. Moreover, the multiple-goal priority considerations of air traffic controllers are also fully considered in this model. The two objectives are to simultaneously minimize delays in arrival and departure flights. Since this problem is NP-hard, a novel polynomial algorithm based on queuing theory is also proposed to obtain acceptable solutions efficiently. Finally, a real-world example is provided to analyze the effect of different times and places of incursion events on the scheduling scheme, which can verify the correctness of the model. Results show that higher runway incursion times lead to longer queue lengths for take-off and landing flights, resulting in more flight delays.

References

[1]	X. M. Dai, J. F. Zhang, P. L. Zhao, Study on multi-objective optimization of aircraft departure sequencing, J. Harbin Uni. Com. (Nat. Sci. Ed.), 35 (2019), 241-245.
[2]	K. K. H. Ng, C. K. M. Lee, F. T. S. Chan, Y. Qin, Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach, Transp. Res. Part E: Logist. Transp. Rev., 106 (2017), 115-136. doi: 10.1016/j.tre.2017.08.006
[3]	B. Sun, M. Wei, W. Wu, B. B. Jing, A novel group decision making method for airport operational risk management, Math. Biosci. Eng. 17 (2020), 2402-2417. doi: 10.3934/mbe.2020130
[4]	J. T. Zhang, W. J. Yang, The optimization based on priority for a mixed arrival-departure aircraft sequencing problem, Oper. Res. Manage. Sci., 27 (2018), 115-122.
[5]	G. Q. Yuan, M. Q. Meng, C. P. Li, Transportation chance constrained model with fuzzy parameters, Appl. Mech. Mater., 135 (2012), 1193-1200.
[6]	Y. F. Zhou, M. H. Hu, Y. Zhang, M. Y. Gao, An uncertainty analysis of arrival aircraft schedule based on Monte-Carlo simulation, J. Transp. Inf. Saf., 34 (2016), 22-28.
[7]	M. Ahmed, S. Alam, M. Barlow, A cooperative co-evolutionary optimisation model for best-fit aircraft sequence and feasible runway configuration in a multi-runway airport, Aerospace, 5 (2018), 345-353.
[8]	A. Faye, Solving the aircraft landing problem with time discretization approach, Eur. J. Oper. Res., 242 (2015), 1028-1038. doi: 10.1016/j.ejor.2014.10.064
[9]	F. Farhadi, A. Ghoniem, M. Al-Salem, Runway capacity management-an empiricalstudy with application to Doha international airport, Transp. Res. Part E: Logist. Transp. Rev., 68 (2014), 53-63. doi: 10.1016/j.tre.2014.05.004
[10]	H. Nazini, T Sasikala, Simulating aircraft landing and takeoff scheduling in distributed framework environment using Hadoop file system, Cluster Comput., 22 (2019), 13463-13471. doi: 10.1007/s10586-018-1980-y
[11]	L. Bianco, P. Dell'Olmo, S. Giordani, Scheduling models for air traﬃc control in terminal areas, J. Sched., 9 (2006), 180-197.
[12]	D. Briskorn, R. Stolletz, A dynamic programming approach for the aircraft landing problem with aircraft classes, Eur. J. Oper. Res., 243 (2015), 61-69. doi: 10.1016/j.ejor.2014.11.027
[13]	R. G. Dear, The dynamic scheduling of aircraft in the near terminal area, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge, Mass., 1976.
[14]	H. N. Psaraftis, A dynamic programming approach for sequencing identical groups of jobs, Oper. Res., 28 (1980), 1347-1359. doi: 10.1287/opre.28.6.1347
[15]	J. E. Beasley, M. Krishnamoorthy, Y. M. Sharaiha, D. Abramson, Scheduling aircraft landings-The static case, Transpor. Sci., 34 (2000), 180-197. doi: 10.1287/trsc.34.2.180.12302
[16]	V. J. Hansen, Genetic search methods in air traﬃc control, Comput. Oper. Res., 31 (2004), 445-459. doi: 10.1016/S0305-0548(02)00228-9
[17]	Y. H. Liu, A genetic local search algorithm with a threshold accepting mechanism for solving the runway dependent aircraft landing problem, Optim. Lett., 5 (2011), 229-245. doi: 10.1007/s11590-010-0203-0
[18]	A. Salehipour, M. Modarres, L. M. Naeni, An efﬁcient hybrid meta-heuristic for aircraft landing problem, Comput. Oper. Res., 40 (2013), 207-213. doi: 10.1016/j.cor.2012.06.004
[19]	G. Bencheikh, J. Boukachour, A. E. H. Alaoui, Improved ant colony algorithm to solve the aircraft landing problem, Int. J. Comput. Theory Eng., 3 (2011), 224-233.
[20]	X. Meng, Z. Ping, L. Chunjin, Sliding window algorithm for aircraft landing problem, CCDC, (2011), 874-879.
[21]	M. Wei, B. Sun, W. Wu, B. B. Jing, A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runway, Math. Biosci. Eng., 17 (2020), 5545-5560. doi: 10.3934/mbe.2020298
[22]	M. Samà, A. D'Ariano, F. Corman, D. Pacciarelli, Coordination of scheduling decisions in the management of airport airspace and taxiway operations, Transport. Res. Proc., 23 (2017), 246-262. doi: 10.1016/j.trpro.2017.05.015
[23]	G. SöLVELING, J. P. CLARKE, Scheduling of airport runway operations using stochastic branch and bound methods, Transp. Res. Part C: Emerg. Technol., 45 (2014), 119-137. doi: 10.1016/j.trc.2014.02.021
[24]	C. S. Venkatakrishnan, A. Barnett, A. M. Odoni, Landings at Logan airport: describing and increasing airport capacity, Transport. Sci., 27 (1993), 211-227. doi: 10.1287/trsc.27.3.211
[25]	Z. N. Zhang, K. X. Liu, Arrival sequencing method based on the alternative routes, Math. Pract. Theory, 49 (2019), 191-198.
[26]	A. Ghoniem, F. Farhadi, M. Reihaneh, An accelerated branch-and-price algorithm for multiple-runway aircraft sequencing problems, Eur. J. Oper. Res., 246 (2015), 34-43. doi: 10.1016/j.ejor.2015.04.019
[27]	A. Ghoniem, F. Farhadi, A column generation approach for aircraft sequencing problems: a computational study, J. Oper. Res. Soc., 66 (2015), 1717-1729. doi: 10.1057/jors.2014.131
[28]	H. Balakrishnan, B. Chandran, Algorithms for scheduling runway operations under constrained position shifting, Oper. Res., 58 (2010), 1650-1665. doi: 10.1287/opre.1100.0869
[29]	Y. Ding, J. Valasek, Aircraft landing scheduling optimization for single runway noncontrolled airports: Static Case, J. Guid. Control Dyn., 30 (2007), 252-255. doi: 10.2514/1.20264
[30]	F. Furini, M. P. Kidd, C. A. Persiani, P. Toth, State space reduced dynamic programming for the aircraft sequencing problem with constrained position shifting, ISCO, (2014), 267-279.
[31]	D. Harikiopoulo, N. Neogi, Polynomial-time feasibility condition for multiclass aircraft sequencing on a single-runway airport, IEEE Trans. Intell. Transp. Syst., 12 (2011), 2-14. doi: 10.1109/TITS.2010.2055856
[32]	A. Lieder, R. Stolletz, Scheduling aircraft take-offs and landings on interdependent and heterogeneous runways, Transp. Res. Part E: Logist. Transp. Rev., 88 (2016), 167-188. doi: 10.1016/j.tre.2016.01.015
[33]	M. A. Dulebenets, A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals, Marit. Bus. Rev., 2 (2017), 302-330. doi: 10.1108/MABR-04-2017-0012
[34]	Z. Z. Liu, Y. Wang, P. Q. Huang, AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation, Inf. Sci., 509 (2020), 400-419. doi: 10.1016/j.ins.2018.06.063
[35]	J. Pasha, M. A. Dulebenets, M. Kavoosi, O. F. Abioye, H. Wang, W. Guo, An optimization model and solution algorithms for the vehicle routing problem with a "factory-in-a-box", IEEE Access, 8 (2020), 134743-134763. doi: 10.1109/ACCESS.2020.3010176
[36]	G. D'Angelo, R. Pilla, C. Tascini, S. Rampone, A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees, Soft Comput., 23 (2019), 11775-11791. doi: 10.1007/s00500-018-03729-y

Reader Comments

Your name:*

Email:*
© 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)