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Crew Recovery Using Machine Learning and Optimization

Due to the irregular nature of flight operations, airlines need to take a range of actions to recover their aircraft and crew schedules. The limited time frames prevent airlines from using a full-scale optimization approach. Consequently, airlines typically apply recovery solutions that can be far from optimal. This study proposes a practical method that combines machine learning and optimization to find improved recovery solutions. Our procedure is based on the idea that the most effective constraints to add to the recovery models without sacrificing the solution quality, can be determined in advance by leveraging the similarities between disruptions. Our experiments show that this approach can reduce solution time significantly while still achieving high-quality solutions.

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