侧向跑道机场滑行路径优化

为了缓解机场场面交通拥挤状况,提高侧向跑道机场场面运行效率,构建了侧向跑道机场航空器滑行路径优化模型.该模型以航空器加权滑行时间和延误等待时间最小为目标,提出了动态优化航班的优先级的优化方案.将航空器的运行规则转化为相应的数学约束条件,根据侧向跑道机场的滑行道调度问题进行算法设计,运用改进的遗传算法对模型进行求解,以航空器的优先级滑行序列和航空器滑行路径为染色体,基于MATLAB对双链染色体进行编码,并对4种滑行冲突与解脱进行分析.以成都天府国际机场为例进行算例分析,与先到先服务序列进行对比,采用优化方案的序列可以节省42 s,并与蚁群算法进行比对,验证了改进的遗传算法的有效性,可以为繁忙机场的滑行调度提供决策支持.

Lateral runway airport based on improved genetic algorithm

To alleviate the traffic congestion on the airport surface and improve the operation efficiency of the lateral run-way airport surface, an optimization model for the aircraft taxiing path at the lateral runway airport was con-structed. Moreover, aiming at the minimum weighted taxiing time and delay-induced waiting time of the aircraft, an optimization scheme was proposed to dynamically optimize flight priority. Specifically, aircraft operating rules were translated into corresponding mathematical constraints, and an algorithm was designed to calculate the taxiway scheduling problem of lateral runway airports. Then the improved genetic algorithm was adopted to solve the model. With aircraft priority taxi sequence and aircraft taxi path as chromosomes, the double-stranded chromosomes were encoded by MATLAB. It was followed by an analysis of four cases of taxiing conflict and relief. Afterward, Chengdu Tianfu International Airport was taken as an example for calculation. The sequence based on the optimized scheme saved 42 seconds compared with the first-come-first-served sequence; while the effectiveness of the improved genetic algorithm was verified by the comparison with the ant colony algorithm. Therefore, the optimized algorithm can provide decision support for taxiing scheduling in busy airports.