关键词: 航空运输, 航空公司分配模型, 模拟退火-自适应粒子群, 枢纽机场, 中转水平

Abstract: To improve the efficiency of cross-terminal transfer in multi-terminal hub airports, this study investigates an optimization model of terminal allocation that determines the airline's distribution layout in each terminal. In order to improve the convenience of airlines, enhance the travel experience of transit passengers, and reduce the operating cost of airports, a multi-objective integer programming model for airline allocation optimization with multiple terminals was established. The model minimizes the annual number of cross-terminal passengers, the number of cross-terminal combinations between airlines of the same airline alliance, the total annual cross-terminal passengers' transfer time, and the annual number of international/Hong Kong/Macau/Taiwan cross-terminal passengers, considering the terminal capacity limit, airline passenger processing capacity limit and other factors. A hybrid optimization algorithm of Simulated Annealing and Adaptive Particle Swarm Optimization (SA-APSO) was designed to solve the model. A hub airport in southwest China was selected for example analysis. Firstly, the SA-APSO algorithm was used to solve small-scale examples. The results showed that the optimized targets were reduced by 14.51%~50.00% respectively, and the objective function values change slightly at different runs, which verifies the effectiveness of the model and algorithm, and the stability of the algorithm. Then, the SA-APSO algorithm was used to solve large-scale examples, and the optimal schemes corresponding to different target weight combinations were obtained. The results showed that the optimized objectives were reduced by 26.86%, 28.33%, 89.91%, and 28.84%, respectively. The results show that: the proposed model and algorithm can fully take into account the interests of airlines, transit passengers, and airports. While meeting the limitations of terminal capacity and passenger handling capacity of airlines, it can improve the efficiency of cross-terminal passenger transfer, reduce airport operating costs, and meet the convenience needs of airlines, which can enhance the attraction of hub airport transit and provide theoretical guidance for the optimization of airline terminal allocation in multi-terminal hub airports.

Key words: air transportation, airline terminal allocation model, Simulated Annealing-Adaptive Particle Swarm Optimization, hub airports, transit levels