基于改进遗传算法的离场航班时刻优化

Optimization of Departure Flight Schedules Based on Improved Genetic Algorithm

为提高机场航班放行正常率,对离场航班时刻进行了智能优化方法研究。考虑进场航班时刻不变、离场容量和航班时刻调整范围受限等约束,构建了以全局时间调整偏差总量最小为目标的离场航班时刻优化模型;为提高优化效率,将遗传算法的交叉概率改进为自适应交叉概率;设计了一种基于改进遗传算法的离场航班时刻优化方法。以兰州中川国际机场全天运行455起降架次为例,对航班时刻进行优化和仿真验证。结果表明,优化的航班时刻相较于原航班时刻,航班平均延误时间降低12.8%;离场航班平均延误时间降低22.3%;离场航班延误架次减少了42.8%;航班放行正常率提高了12%。采用基于自适应交叉概率的遗传算法可有效降低了航班延误和提高航班放行正常率。

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.