Volume 49 Issue 6
Jun.  2023
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WANG X L,XU Y F,XUE Y C. Evaluation and optimization of departure flight schedule stability of airport group[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1331-1341 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0462
Citation: WANG X L,XU Y F,XUE Y C. Evaluation and optimization of departure flight schedule stability of airport group[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1331-1341 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0462

Evaluation and optimization of departure flight schedule stability of airport group

doi: 10.13700/j.bh.1001-5965.2021.0462
Funds:

National Key R&D Program of China (2020YFB1600101); Natural Science Key Project of Tianjin Municipal Education Commission (2020ZD01) 

More Information
  • Corresponding author: E-mail:xinglong1979@163.com
  • Received Date: 13 Aug 2021
  • Accepted Date: 29 Oct 2021
  • Publish Date: 11 Nov 2021
  • As China’s aviation traffic keeps growing, issues including dwindling flight schedule resources and major flight delays in airport clusters are rapidly becoming more and more prevalent. It is necessary to thoroughly study flight schedule optimization in airport groups. On the basis of defining the concept of departure flight schedule stability of airport groups, this paper puts forward six evaluation indexes of departure flight schedule stability of airport groups, such as departure flight delay rate and average delay time, and evaluates the stability quality by using improved TOPSIS (a technique for order preference by similarity to ideal solution). Following the establishment of the airport group’s departure flight schedule optimization model and the selection of an improved particle swarm optimization algorithm to optimise the model, the flight plans before and after optimization are contrasted using the stability quality as the benchmark. Finally, taking Beijing-Tianjin-Hebei airport group as an example, the simulation results show that the proposed optimization model and algorithm can reduce the average delay time of departure flights at Beijing airport by 18.8 s and the average delay rate by 9.9%; The average delay time of busy routes is reduced by 12.7 s, and the average delay rate is reduced by 3.0%, which effectively reduces the overall delay level of Beijing-Tianjin-Hebei airport group and improves the stability of departure flight schedule of the airport group.

     

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