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摘要: 为了提升大型繁忙机场的运行效率, 考虑了多跑道的运行条件和安全要求等因素, 以最小航班总延误为目标函数, 以最大位置偏移为约束条件, 引入滚动时域控制策略, 建立了航班动态排序模型。针对多跑道航班调度问题的特点, 分别采用基于滚动时域控制策略的遗传算法和现有的先到先服务算法求解模型。计算结果表明: 当航班正常时, 采用现有的先到先服务算法, 航班总延误为1 712s, 采用基于滚动时域控制策略的遗传算法, 航班总延误为1 080s, 与先到先服务算法相比, 延误时间减小37.0%;当航班不正常时, 采用现有的先到先服务算法, 航班总延误为1 658s, 采用基于滚动时域控制策略的遗传算法, 航班总延误为969s, 与先到先服务算法相比, 延误减小41.5%。可见, 基于滚动时域控制策略的遗传算法有效。Abstract: In order to improve the operation performance of large busy airport, some factors of multi-runways such as operation condition and safety requirement were considered. The minimum total flight delay was taken as objective function, the maximum position shift was taken as constraint condition, receding horizon control(RHC) strategy was introduced, and the dynamic flight sequencing model was established. Aiming at the characteristics of flight scheduling problem for multi-runways, the model was solved by using the genetic algorithm based on RHC strategy(RHC-GA) and the existing first come first serve(FCFS) algorithm respectively. Calculation result shows that when flights are normal, the total flight delay is 1 712 s by using FCFS algorithm. The total flight delay is 1 080 s by using RHC-GA, and reduces by 37.0% compared with the result of FCFS algorithm. When flights are not normal, the total flight delay is 1 658 s by using FCFS algorithm. The total flight delay is 969 s by using RHC-GA, and reduces by 41.5% compared with the result of FCFS algorithm. So RHC-GA is effective.
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Key words:
- air transportation /
- traffic flow management /
- flight sequencing /
- RHC strategy /
- genetic algorithm /
- chromosome coding
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