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Article type: Research Article
Authors: Tian, Wena; * | Zhang, Yininga | Fang, Qina | Liu, Weidongb
Affiliations: [a] State Key Laboratory of Air Traffic Management System, Nanjing University of Aeronautics and Astronautics, Nanjing, China | [b] The Second Research Institute of CAAC, Chengdu, China
Correspondence: [*] Corresponding author. Wen Tian, State Key Laboratory of Air Traffic Management System, Nanjing University of Aeronautics and Astronautics, Jiangning Campus, Nanjing, China. E-mail: [email protected].
Abstract: In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.
Keywords: Air traffic flow management, resource allocation, collaborative trajectory options program (CTOP), multi-objective optimization, genetic algorithm
DOI: 10.3233/JIFS-233588
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
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