Data analytics for robust crew pairing

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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.