Optimization of Departure Flight Schedules Based on Improved Genetic Algorithm
In order to improve the on-time departure rate at the airport,a study was conducted on intelligent optimization methods for departure flight schedules.Considering constraints such as fixed arrival flight times,limited departure capacity,and flight schedule adjustment ranges,an optimization model for departure flight schedules was constructed with the objective of minimizing the total global time adjustment deviation.To enhance the optimization efficiency,the crossover probability of the genetic algorithm was adapted for self-adjustment.An approach based on an improved genetic algorithm for optimizing departure flight schedules was designed.Taking Lanzhou Zhongchuan International Airport as an example with a daily operation of 455 departing and landing,flight schedules were optimized and simulated.The results show that,compared to the original flight schedules,the optimized flight schedules reduce the average flight delay time by 12.8%.The average departure flight delay time decreases by 22.3%,and the number of delayed departure flights decreases by 42.8%.The on-time departure rate of flights improves by 12%.The use of a genetic algorithm with self-adjusting crossover probability effectively reduces flight delays and improves the on-time departure rate of flights.
air traffic managementairport controlflight schedule optimizationimproved genetic algorithmadaptive crossover probability
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