Volume 26 Issue 2
Feb.  2026
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JIANG Yu, LIU Hong-yang, LI Han-lu, YUAN Ye, XUE Qing-wen. Optimization model for trunk-regional-general hierarchical air transportation network with stratified demand[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 140-154. doi: 10.19818/j.cnki.1671-1637.2026.147
Citation: JIANG Yu, LIU Hong-yang, LI Han-lu, YUAN Ye, XUE Qing-wen. Optimization model for trunk-regional-general hierarchical air transportation network with stratified demand[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 140-154. doi: 10.19818/j.cnki.1671-1637.2026.147
shu

Optimization model for trunk-regional-general hierarchical air transportation network with stratified demand

doi: 10.19818/j.cnki.1671-1637.2026.147
  • 1.

    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

  • 2.

    College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Changzhou 213300, Jiangsu, China

Funds:

National Natural Science Foundation of China 52372298

National Natural Science Foundation of China 52302517

More Information
  • Corresponding author: JIANG Yu, professor, PhD, jiangyu07@nuaa.edu.cn
  • Received Date: 2025-07-31
  • Accepted Date: 2026-01-15
  • Rev Recd Date: 2025-12-11
  • Publish Date: 2026-02-28
    Abstract
  • The trunk-regional-general hierarchical air transportation network was taken as the research object, and modeling consistent with its operational characteristics was conducted to construct and solve a hierarchical hub location model with stratified demand. The demand layer was employed to distinguish various demands. To minimize the total cost, including transportation costs, hub construction fixed costs, and fixed costs for establishing flight connections, an r-allocation hierarchical hub location model permitting non-hub direct connections and regular hub direct connections was built. According to the topological characteristics of air transportation networks, a VNS-GA hybrid heuristic algorithm based on an alternating mechanism was designed by combining the advantages of the VNS algorithm and the genetic algorithm (GA). Hub selection and demand node allocation were optimized by VNS, while direct connections were optimized by GA. The modeling and solution were carried out for the two classical datasets, namely, Civil Aeronautics Board (CAB) and Australia Post (AP), as well as the Yangtze River Delta regional airport data in China. The existing model and the stratified demand model were compared to verify the effectiveness of the algorithm and analyze the parameter sensitivity. According to the research results, in the small-scale case of 15 nodes, the stratified demand model reduces the total cost by 9.23%. In the small-scale case of 25 nodes, the alternating VNS-GA algorithm has a gap with the optimum solution of no more than 2.56% under various parameter configurations, with the average solution time only 10.78% of that of the commercial solver. In the medium and large-scale case of 100 nodes, sensitivity analysis shows that the setting of stratified weight coefficients has a great impact on the optimization results. The r-allocation strategy can reduce the total cost but with obvious diminishing marginal benefits. In the semi-empirical experiment of the Yangtze River Delta region, the model can reduce the cost by 2.75% while adding 50 direct connections. This verifies the feasibility and effectiveness of the model in optimizing trunk-regional-general hierarchical air transportation networks.

     

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