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Optimization of airport ground operations integrating genetic and dynamic flow management algorithms

机译：集成遗传和动态流管理算法的机场地面运营优化

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摘要

This paper presents a new method for automatically finding the best routes and schedules for airport ground operations within a decision support system for tower controllers, a hard real-world application. It explores the potential advantages of hybridizing two complementary types of algorithmic approaches to find solutions as fast as possible: a genetic algorithm and a time-space dynamic flow management algorithm. An integrated system to combine the strengths of each algorithm and exploit their complementary nature has been analyzed. The proposed flow-management algorithm deterministically optimizes an oversimplified problem, while the genetic algorithm is able to search within a more realistic representation of the real problem, but success is not always guaranteed if the search space grows. The performance of this combination is illustrated using simulated samples of a real-world scenario: ground operations at Madrid Barajas International Airport.
机译：本文提出了一种新方法，可在塔式控制器的决策支持系统中自动找到最佳的机场地面运营路线和时间表，这是一项艰苦的实际应用。它探讨了将两种互补类型的算法方法进行混合以尽可能快地找到解决方案的潜在优势：遗传算法和时空动态流管理算法。分析了一种综合系统，该系统结合了每种算法的优势并利用其互补性。提出的流程管理算法确定性地优化了过于简化的问题，而遗传算法能够在真实问题的更真实表示中进行搜索，但是如果搜索空间增加，则并不总是能够保证成功。通过结合真实场景的模拟样本来说明这种组合的性能：马德里巴拉哈斯国际机场的地面运营。

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