Robustness of two air traffic scheduling approaches to departure uncertainty
Robustness of two air traffic scheduling approaches to departure uncertainty
复制标题
两种空中交通调度方法对出发不确定性的鲁棒性
DOI：
10.1109/dasc.2011.6095996
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
J. Rios
中科院分区：
文献类型：
--
作者：
A. Agogino;J. Rios
线性规划方法和基于非线性进化算法的优化技术已被证明在管理大规模空中交通流量问题中是有效的。然而，许多这些算法假设完美的知识，因此这些算法的鲁棒性存在的不确定性是值得怀疑的。由于这些方法的实际应用要求它们在不确定性下有效（即产生很少的意外容量违规），因此在这种条件下对其进行测试至关重要。在本文中，我们测试的有效性存在的不确定性的二进制编程方法和一种新的，快速学习的进化算法。具体来说，我们改变了这些算法训练的假设起飞时间，并在合并与历史数据一致的起飞延迟时测试得到的解决方案。实验结果表明，在没有不确定性的情况下，这两套算法都能够快速产生解决方案，几乎没有违规行为。在存在不确定性的情况下，算法的性能相对于增加的延迟量而降低，但仍然非常好。即使不确定性非常高，预期延迟也不会增加超过30%。
Linear programming methods and non-linear, evolutionary algorithm-based optimization techniques have been shown to be effective in managing large-scale air traffic flow problems. However, many of these algorithms assume perfect knowledge therefore the robustness of these algorithms in the presence of uncertainties is questionable. Since real-world application of these methods require them to be effective under uncertainty (i.e. produce few unexpected capacity violations), it is critical that they are tested in such conditions. In this paper we test the effectiveness in the presence of uncertainty of a binary programming approach and a novel, fast-learning evolutionary algorithm. Specifically we change the assumed takeoff times on which these algorithms are trained, and test the resulting solutions when takeoff delays that are consistent with historical data are incorporated. Experimental results show that without uncertainty, both sets of algorithms are able to quickly produce solutions with few to no violations. In the presence of uncertainty, the performance of the algorithms degrade with respect to the amount of delay added, but are still very good. Even when uncertainty is extremely high, the expected delay is never increased more than 30%.