Data analytics for robust crew pairing

列生成和分支和价格被广泛接受的方法，包括机组配对优化的几个航空公司的运营规划问题。虽然过去60年来发展的大多数研究都假定已知的和恒定的飞行特性，但实际上这很难做到。航空公司每天都面临着这些特征的变化，比如航班延误和机组人员缺勤。特别是，机组人员的时间表受到很大的影响，因为严格的规定，机组人员的安全，劳动协议等，最近的研究地址的不确定性，在飞行时间的建模计划恢复策略，并使用强大的或随机优化，假设不确定的参数。随着历史飞行数据的丰富，利用机器学习工具的进步来开发飞行特性的预测模型，使用预测信息来增强现有的机组配对优化解决方案方法，以及开发集成学习和优化的新模型和解决方案，这是一个巨大的机会。

Column generation and branch-and-price are well accepted approaches for several airline operations planning problems including crew pairing optimization. While most of the research developed over the last 60 years assumes known and constant flight characteristics, this is hardly true in practice. Airlines face daily changes in these characteristics like flight delays and crew absenteeism. In particular, crew schedules are highly impacted because of the strict regulations on crew safety, labor agreements, etc. Recent research addresses uncertainty in flight durations by modelling schedule recovery strategies and using robust or stochastic optimization, where assumptions are made on the uncertain parameters. With the abundance of historical flight data, there is a huge opportunity to leverage advances in machine learning tools to develop prediction models of flight characteristics, to augment existing crew pairing optimization solution methods using predicted information, and to develop new models and solutions that integrate learning and optimization.