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Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm


  • Received: 05 July 2021 Accepted: 10 September 2021 Published: 22 September 2021
  • In this study, a multi-objective optimized mathematical model of stand pre-allocation is constructed with the shortest travel distance for passengers, the lowest cost for airlines and the efficiency of stand usage as the overall objectives. The actual data of 12 flights at Lanzhou Zhongchuan Airport are analyzed by application and solved by simulated annealing algorithm. The results of the study show that the total objective function of the constructed model allocation scheme is reduced by 40.67% compared with the actual allocation scheme of the airport, and the distance traveled by passengers is reduced by a total of 4512 steps, while one stand is saved and the efficiency of stand use is increased by 31%, in addition to the reduction of airline cost by 300 RMB. In summary, the model constructed in the study has a high practical application value and is expected to be used for airport stand pre-allocation decision in the future.

    Citation: Ningning Zhao, Mingming Duan. Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 8314-8330. doi: 10.3934/mbe.2021412

    Related Papers:

  • In this study, a multi-objective optimized mathematical model of stand pre-allocation is constructed with the shortest travel distance for passengers, the lowest cost for airlines and the efficiency of stand usage as the overall objectives. The actual data of 12 flights at Lanzhou Zhongchuan Airport are analyzed by application and solved by simulated annealing algorithm. The results of the study show that the total objective function of the constructed model allocation scheme is reduced by 40.67% compared with the actual allocation scheme of the airport, and the distance traveled by passengers is reduced by a total of 4512 steps, while one stand is saved and the efficiency of stand use is increased by 31%, in addition to the reduction of airline cost by 300 RMB. In summary, the model constructed in the study has a high practical application value and is expected to be used for airport stand pre-allocation decision in the future.



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    [1] S. H. Kim, E. Féron, J. P. Clarke, A. Marzuoli, D. Delahaye, Airport gate scheduling for passengers, aircraft, and operations, J. Air Transp., 25 (2017), 109-114. doi: 10.2514/1.D0079
    [2] C. Yu, D. Zhang, H. Y. Lau, MIP-based heuristics for solving robust gate assignment problems, Comput. Ind. Eng., 93 (2016), 171-191. doi: 10.1016/j.cie.2015.12.013
    [3] S. Liu, W. H. Chen, J. Liu, Robust assignment of airport gates with operational safety constraints, Int. J. Autom. Comput., 13 (2016), 31-41. doi: 10.1007/s11633-015-0914-x
    [4] W. Deng, H. Zhao, X. Yang, J. Xiong, M. Sun, B. Li, Study on an improved adaptive PSO algorithm for solving multi-objective position assignment, Appl. Soft Comput., 59 (2017), 288-302. doi: 10.1016/j.asoc.2017.06.004
    [5] S. Yang, Study on optimized position scheduling of airport based on flight delay, Xi'an: Xi'an University of Technology, 2018.
    [6] W. Deng, M. Sun, H. Zhao, B. Li, C. Wang, Study on an airport position assignment method based on improved ACO algorithm, Kybernetes, 2018.
    [7] M. Bagamanova, M. M. Mota, A multi-objective optimization with a delay-aware component for airport stand allocation, J. Air Transp. Manag., 83 (2020), 101757. doi: 10.1016/j.jairtraman.2019.101757
    [8] J. Lin, X. Ding, H. Li, J. Zhou, Bilevel programming model and algorithms for flight gate assignment problem, Aeronaut. J., 124 (2020), 1667-168. doi: 10.1017/aer.2020.40
    [9] U. Benlic, , E. K. Burke, J. R. Woodward, Breakout local search for the multi-objective gate allocation problem, Comput. Oper. Res., 78 (2017), 80-93. doi: 10.1016/j.cor.2016.08.010
    [10] S. Srinivas, S. Ramachandiran, Discovering airline-specific business intelligence from online passenger reviews: an unsupervised text analytics approach, preprint, arXiv: 2012.08000.
    [11] S. Rajendran, S. Srinivas, T. Grimshaw, Predicting demand for air taxi urban aviation services using machine learning algorithms, J. Air Transp. Manag., 92 (2021), 102043. doi: 10.1016/j.jairtraman.2021.102043
    [12] X. Yue, W. Zhang, UAV path planning based on k-means algorithm and simulated annealing algorithm, in 2018 37th Chinese Control Conference (CCC), Spring, (2018), 2290-2295.
    [13] S. Kirkpatrick, C. D. Gelatt Jr, M. P. Vecchi, Optimization by simulated annealing, in Readings in Computer Vision, Spring, (1987), 606-615.
    [14] W. X. Xing, J. X. Xie, Modern optimization calculation method, Beijing: Tsinghua University Press, 2003.
    [15] M. Liang, Hybrid heuristic algorithm for TSP and its extension problem, J. U. Shanghai Sci. Technology, 1999.
    [16] D. W. Hu, Z. Q. Zhu, Y. Hu, A simulated annealing algorithm for vehicle routing problem, Chin. J. Highway Sci., 4 (2006).
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