Abstract
Flight schedule optimization is critical to improving air traffic flow management. This paper considered the impact of weather conditions on the capacity of the airport and airspace, and combined ground-holding and air-holding strategies to establish a multi-objective short-term airport group flight schedule optimization model. At the same time, this paper designed a non-dominated sorting genetic algorithm (NSGA-II algorithm) with an elite strategy, and verified the case with the Yangtze River Delta airport group. The results show that the model can reduce the delay time and delays of the airport group by nearly 25%. The innovation point of this paper is to further consider the impact of weather factors on flights in reality, and improve the traditional genetic algorithm for better solution, which has practical significance. The method can effectively improve the level of air traffic flow management.
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