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针对大规模航班延误的问题,本文提出了基于多目标的改进遗传算法的离港调度方法.综合考虑航班的延误时间、调度成本和机场吞吐量等多种因素对航班调度的影响,来使调度结果最贴近用户设定的各因素比例.首先使用模糊聚类方法对航班离港调度资源进行聚类分析,得到资源的聚类结果后,结合免疫算法和模拟退火算法对遗传算法进行了改进,从而获得比较理想的航班离港调度方案.最后,结合中国某机场的实际数据,对算法进行了验证,证明了算法的可行性和有效性.
In order to solve the problem of large-scale flight delays, an outbound scheduling method based on multi-objective and improved genetic algorithm is proposed in this paper.Considering the impact of flight delays, scheduling costs and airport throughput on flight scheduling, The result is closest to the proportion of each factor set by the user.Firstly, the fuzzy clustering method is used to cluster the departure dispatch resources of the flight to get the clustering results of the resources, the improved genetic algorithm is combined with immune algorithm and simulated annealing algorithm, So as to get an ideal flight departure dispatch plan.Finally, combining the actual data of an airport in China, the algorithm is validated to prove the feasibility and effectiveness of the algorithm.