| Summary: | Flight network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the air transport network is analyzed with a multi-objective genetic algorithm to reduce the number of airways and to aggregate the passengers and also to reduce route changes and the travel time fortravelers. The proposed topology model of this study is based on the combination of two topologies – point-to-point and hub-and-spoke – with multiple goals for decreasing in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. Four state-of-the-art Multi-objective Genetic Algorithms (MOGAs) are considered for comparison studies and are tested and assessed in data of the Iran airline industry in 2018, as an experiment to real-world applications. Using the combination of point-to-point and hub-and-spoke topologies can improve the performance of the MOGA to solve a network-wide flight trajectory planning. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 km) and up to 18%, respectively. The proposed model also suggests that the current air routes of Iran can be decreased to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 km) and 5%, respectively. The simulation results show the potential benefits of the proposed model and the advantages of it. Optimizing the structure of the flight network can significantly reduce operational cost while ensuring the operation safety. According to the results, the multi-objective optimization model is successfully able to precisely design the efficiently optimized airline topologies.
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