论文标题
航空公司乘员安排的飞行连接预测以构建用于或优化器的初始群集
Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
论文作者
论文摘要
我们提出了一项案例研究，该案例研究使用机器学习分类算法在航空公司乘员配对问题的背景下，基于柱子的生成初始化大型商业求解器（GENCOL），其中只有1％的少量节省转化为一家大型航空公司的数十亿美元的年收入。在模仿学习框架下，我们专注于预测船员的下一次连接飞行的问题，该飞行是从历史数据中训练的多类分类问题，并设计了一种适应性的神经网络方法，该方法可实现高精度（总体上为99.7％或在艰苦实例上为82.5％）。我们通过使用简单的启发式方法将飞行连接预测结合起来，形成可以在Gencol求解器中馈送的初始船员配对簇，从而证明了方法的有用性，从而可以提高10倍的速度，并节省高达0.2％的成本。
We present a case study of using machine learning classification algorithms to initialize a large-scale commercial solver (GENCOL) based on column generation in the context of the airline crew pairing problem, where small savings of as little as 1% translate to increasing annual revenue by dozens of millions of dollars in a large airline. Under the imitation learning framework, we focus on the problem of predicting the next connecting flight of a crew, framed as a multiclass classification problem trained from historical data, and design an adapted neural network approach that achieves high accuracy (99.7% overall or 82.5% on harder instances). We demonstrate the usefulness of our approach by using simple heuristics to combine the flight-connection predictions to form initial crew-pairing clusters that can be fed in the GENCOL solver, yielding a 10x speed improvement and up to 0.2% cost saving.
