A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing
A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing
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DOI：
10.1145/3205455.3205558
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发表时间：
2018-07
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通讯作者：
A. Brownlee;J. Woodward;Michal Weiszer;Jun Chen
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文献类型：
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作者：
A. Brownlee;J. Woodward;Michal Weiszer;Jun Chen
随着航空旅行需求的增加和机场设施的超载，低效的机场滑行操作是不必要的燃料燃烧和污染的重要来源。虽然滑行只是飞行的一小部分，但飞机发动机并没有针对滑行速度进行优化，因此对总体燃油消耗的贡献不成比例。滑行延误也浪费了稀缺的机场资源，让乘客感到沮丧。因此，减少滑行时间是一项重要的投资。基于A* 的最短路径精确算法将航线分配给飞机，使飞机保持安全距离，已被证明可以产生有效的滑行航线。然而，这种方法取决于选择飞机分配航线的顺序。找到正确的顺序来分配航线的飞机本身是一个组合优化问题。我们应用滚动窗口的方法，结合遗传算法的排列这个问题，在三个忙碌繁忙的机场在现实世界中的情况。这是一个详尽的方法相比，在小滚动窗口，和传统的先到先得的顺序。我们表明，GA是能够减少整体出租车时间相对于其他方法。
With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself. We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-first-served ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches.