Volume 22 Issue 1
Feb.  2022
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JIANG Yu, LIU Zhen-yu, HU Zhi-tao, WU Wei-wei, WANG Zhe. Coordinated scheduling model of arriving aircraft at large airport[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 205-215. doi: 10.19818/j.cnki.1671-1637.2022.01.017
Citation: JIANG Yu, LIU Zhen-yu, HU Zhi-tao, WU Wei-wei, WANG Zhe. Coordinated scheduling model of arriving aircraft at large airport[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 205-215. doi: 10.19818/j.cnki.1671-1637.2022.01.017
shu

Coordinated scheduling model of arriving aircraft at large airport

doi: 10.19818/j.cnki.1671-1637.2022.01.017
  • 1.

    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China

  • 2.

    Beijing Capital International Airport Co., Ltd., Beijing 100621, China

Funds:

National Natural Science Foundation of China U1933118

National Natural Science Foundation of China U2033205

More Information
  • Author Bio:

    Jiang Yu(1975-), female, associate professor, PhD, jiangyu07@nuaa.edu.cn

  • Received Date: 2021-09-10
  • Publish Date: 2022-02-25
    Abstract
  • The problem of coordinated scheduling of arriving aircraft at large airports was studied to reduce the total delay and taxi times of arriving aircraft. The minimum weighted sum of runway sequencing time span and total delay time, the fewest number of flights assigned to remote gates, and the shortest taxi time of arriving aircraft were taken as the objective functions, respectively. A forward coordinated scheduling model for the systems of runway, gate, and taxiway was constructed. A gate re-adjustment model was used to perform the reverse optimization of the taxiway by adjusting the gate assignments of aircrafts with long taxi times. A genetic algorithm (GA) with improved gene coding was designed to prevent the generation of unfeasible solutions and to improve efficiency. Simulation results show that, compared with the first-come-first-serve strategy, the improved GA reduces the runway sequencing time of arriving aircraft by 20 s and the total delay time by 21% from 254 350 s to 199 760 s, respectively. Compared with the ant colony optimization, the improved GA reduces the total delay time by 20 060 s (9%) and produces a smoother iteration curve. After 12 iterations of the improved GA, all arrival aircrafts can be assigned to bridge gates. The total taxi time of 18 arriving aircrafts decreases by 9% from 4 575 s to 4 145 s, and only 3 taxi conflicts occur. 11 aircrafts choose the shortest routes, and only 3 aircrafts have extra taxi time of more than 40 s. After gate adjustment, the total extra taxi time decreases by 58 s or 27%. Therefore, the proposed coordinated scheduling model for arriving aircraft can improve the operational efficiency of large airports and provide decision-making support for airfield resource management. 5 tabs, 11 figs, 31 refs.

     

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