Research article Special Issues

A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways

  • Received: 29 May 2020 Accepted: 30 July 2020 Published: 17 August 2020
  • This study proposes a multi-objective mixed integer linear programming (MOMILP) model for assigning a set of flights to different runways and determining their actual arrival and departure times. The proposed model envisages unique operation model of each runway (i.e., takeoff, landing, or mixed takeoff and landing). Further, interference in two flights between adjacent runways are also fully considered in this model. The work aims at reveal the optimal relationship between traffic stream characteristics, operation mode of each runway and flight scheduling to simultaneously minimizing flight delays and maximizing runway utilization. Since the problem of interest has a non-deterministic polynomial (NP-hard) complexity, a heuristic-based non-dominated sorting genetic algorithm (NSGA-Ⅱ) is also presented to find Pareto-optimal solutions in a reasonable amount of time, where coding structure and heuristic algorithm for producing initial population are defined. Finally, a real-world example is provided to compare the difference in quality between the proposed and traditional models, and reveal changes in trends between delay time of flights and idle time of the runways, which can verify the correctness of the model.

    Citation: Ming Wei, Bo Sun, Wei Wu, Binbin Jing. A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 5545-5560. doi: 10.3934/mbe.2020298

    Related Papers:

  • This study proposes a multi-objective mixed integer linear programming (MOMILP) model for assigning a set of flights to different runways and determining their actual arrival and departure times. The proposed model envisages unique operation model of each runway (i.e., takeoff, landing, or mixed takeoff and landing). Further, interference in two flights between adjacent runways are also fully considered in this model. The work aims at reveal the optimal relationship between traffic stream characteristics, operation mode of each runway and flight scheduling to simultaneously minimizing flight delays and maximizing runway utilization. Since the problem of interest has a non-deterministic polynomial (NP-hard) complexity, a heuristic-based non-dominated sorting genetic algorithm (NSGA-Ⅱ) is also presented to find Pareto-optimal solutions in a reasonable amount of time, where coding structure and heuristic algorithm for producing initial population are defined. Finally, a real-world example is provided to compare the difference in quality between the proposed and traditional models, and reveal changes in trends between delay time of flights and idle time of the runways, which can verify the correctness of the model.


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    [1] 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.
    [2] S. L. Wu, P. C. Chen, K. Y. Chang, C. C. Huang, Robust gain-scheduled control for vertical/short take-off and landing aircraft in hovering with time-varying mass and moment of inertia, Proc. Inst. Mech. Eng. Part G., 222 (2008), 473-482.
    [3] H. Nazini, T. Sasikala, Simulating aircraft landing and takeoff scheduling in distributed framework environment using Hadoop file system, Cluster. Comput., 22 (2019), 13463-13471.
    [4] Y. Ding, J. Valasek, Aircraft landing scheduling optimization for single runway noncontrolled airports: Static Case, J. Guid. Control Dynam., 30 (2007), 252-255.
    [5] M. Ahmed, S Alam, M. Barlow, A cooperative co-evolutionary optimization model for best-fit aircraft sequence and feasible runway configuration in a multi-runway airport, Aerospace, 5 (2018), 345-353.
    [6] L. Bianco, P. Dell'Olmo, S. Giordani, Scheduling models for air trafic control in terminal areas, J. Scheduling, 9 (2006), 180-197.
    [7] D. Briskorn, R. Stolletz, A dynamic programming approach for the aircraft landing problem with aircraft classes, Eur. J. Oper. Res., 43 (2015), 61-69.
    [8] R. G. Dear, The dynamic scheduling of aircraft in the near terminal area, MIT Libraries, (1976).
    [9] H. N. Psaraftis, A dynamic programming approach for sequencing identical groups of jobs, Oper. Res., 28 (1980), 1347-1359.
    [10] V. J. Hansen, Genetic search methods in air traffic control, Comput. Oper. Res., 31 (2004), 445- 459.
    [11] H. Pinol, J. E. Beasley, Scatter search and bionomic algorithms for the aircraft landing problem, Eur. J. Oper. Res., 171 (2006), 439-462.
    [12] A. Salehipour, L. M. Naeni, H. Kazemipoor, Scheduling aircraft landings by applying a variable neighborhood descent algorithm: runway-dependent landing time case, J. Appl. Oper. Res., 1 (2009), 39-49.
    [13] 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.
    [14] 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.
    [15] A. Salehipour, M. Modarres, L. Moslemi Naeni, An efficient hybrid meta-heuristic for aircraft landing problem, Comput. Oper. Res., 40 (2013), 207-213.
    [16] A. Faye, Solving the aircraft landing problem with time discretization approach, Eur. J. Oper. Res., 242 (2015), 1028-1038.
    [17] A. T. Ernst, M. Krishnamoorthy, R. H. Storer, Heuristic and exact algorithms for scheduling aircraft landings, Networks, 34 (1999), 229-241.
    [18] J. E. Beasley, M. Krishnamoorthy, Y. M. Sharaiha, D. Abramson, Scheduling aircraft landings - The static case, Transport. Sci., 34 (2000), 180-197.
    [19] H. N. Psaraftis, A dynamic programming approach for sequencing groups of identical jobs, Oper. Res., 28 (1980), 1347-1359.
    [20] C. S. Venkatakrishnan, A. Barnett, A. M. Odoni, Landings at Logan airport: Describing and increasing airport capacity, Transport. Sci., 27 (1993), 211-227.
    [21] 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.
    [22] F. Furini, M. P. Kidd, C. A. Persiani, P. Toth, State space reduced dynamic programming for the aircraft sequencing problem with constrained position shifting, Int. Symp. Comb. Optim. (ISCO), 2014 (2014), 267-279.
    [23] 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.
    [24] A. Ghoniem, F. Farhadi, A column generation approach for aircraft sequencing problems: A computational study, J. Oper. Res. Soc., 66 (2015), 1717-1729.
    [25] H. Balakrishnan, B. Chandran, Algorithms for scheduling runway operations under constrained position shifting, Oper. Res., 58 (2010), 1650-1665.
    [26] 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.
    [27] A. Lieder. R. Stolletz, Scheduling aircraft take-offs and landings on interdependent and heterogeneous runways, Transport. Res. E-Log., 88 (2016), 67-188.
    [28] M. Samà, A. D'Ariano, F. Corman, D. Pacciarelli, Coordination of scheduling decisions in the management of airport airspace and taxiway operations. Transport. Res. Pro., 23 (2017), 246- 262.
    [29] J. Jemai M. Zekri K. Mellouli, An NSGA-II algorithm for the green vehicle routing problem, Evo. Comput. Com. Opt., 2012 (2012), 37-48.
    [30] M. Wei, T. Liu, B. Sun, B. B. Jing, Optimal integrated model for feeder transit route design and frequency-setting problem with stop selection, J. Adv. Transport., 2020 (20200.
    [31] A. Slowik, H. Kwasnicka, Nature inspired methods and their industry Applications-Swarm intelligence algorithms, IEEE T. Ind. Inform., 14 (2018), 1004-1015.
    [32] M. A. Dulebenets, A comprehensive evaluation of weak and strong mutation mechanisms in evolutionary algorithms for truck scheduling at cross-docking terminals, IEEE Access, 6 (2018).
    [33] X. Zhao, C. Wang, J. Su, J. Wang, Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system, Renew. Energ., 134 (2019), 681-697.
    [34] L. Brezonik, I. Fister, V. Podgorelec, Swarm intelligence algorithms for feature selection: A review, Appl. Sci., 8 (2018), 1521.
    [35] M. A. Dulebenets, A delayed start parallel evolutionary algorithm for just-in-time truck scheduling at a cross-docking facility, Int. J. Prod. Econ., 212 (2019), 236-258.
    [36] H. Anandakumar, K. Umamaheswari, A bio-inspired swarm intelligence technique for social aware cognitive radio handovers, Comput. Electr. Eng., 71 (2018), 925-937.
    [37] T. Li, G. Kou, Y. Peng, Y. Shi, Classifying with adaptive hyper-spheres: An incremental classifier based on competitive learning, IEEE Trans. Syst. Man Cybern. Syst., 50 (2020), 1218-1229.
    [38] G. Kou, C. S. Lin, A cosine maximization method for the priority vector derivation in AHP, Eur. J. Oper. Res., 235 (2014), 225-232.
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