Aircraft recovery, a critical step in airline operations recovery, aims to minimize the cost of disrupted aircraft schedules. The exact methods for aircraft recovery are computationally expensive and operationally infeasible in practice. Heuristics and hybrid approaches offer faster solutions but have inconsistent solution quality, often leading to large losses. We propose a supervised machine learning approach to accelerate aircraft recovery by pruning the solution space of the optimization problem. It leverages similarities with previously solved problem instances through an offline model-training phase, identifies components of the optimal solutions for new problem instances in the online phase, and links them to the optimization model to rapidly generate high-quality solutions. Computational results, from multiple historical disruption instances for a large US airline, demonstrate that this approach significantly outperforms exact methods on computational runtime while producing similarly high-quality solutions. It also outperforms existing heuristics due to its ability to prune solution spaces in a more principled manner, leading to higher quality solutions in similarly short runtimes. For a runtime budget of two minutes, our approach provides a solution within 1.5% of the true optimal cost, resulting in an average daily saving of over $390,000 compared to all existing approaches. The main drivers of these improvements are explainable in terms of key airline operational metrics and are validated through extensive sensitivity and robustness tests.

中文翻译：

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一种机器学习方法，用于减少飞机中断恢复中的解决方案空间


飞机恢复是航空公司运营恢复的关键步骤，旨在最大限度地降低飞机时刻表中断的成本。飞机回收的确切方法在实践中计算成本高昂且操作上不可行。启发式和混合方法提供更快的解决方案，但解决方案质量不一致，通常会导致巨大的损失。我们提出了一种监督式机器学习方法，通过修剪优化问题的解空间来加速飞机恢复。它通过离线模型训练阶段利用与以前解决的问题实例的相似性，在在线阶段为新的问题实例确定最优解的组件，并将它们与优化模型联系起来，以快速生成高质量的解决方案。来自美国一家大型航空公司的多个历史中断实例的计算结果表明，这种方法在计算运行时上明显优于精确方法，同时产生类似的高质量解决方案。它还优于现有的启发式方法，因为它能够以更有原则的方式修剪解决方案空间，从而在同样短的运行时间内获得更高质量的解决方案。对于两分钟的运行时预算，我们的方法提供了与真正最佳成本相差 1.5% 以内的解决方案，与所有现有方法相比，平均每天可节省超过 390000 USD。这些改进的主要驱动因素可以用关键的航空公司运营指标来解释，并通过广泛的敏感性和稳健性测试得到验证。