Crew expenses are one of the major cost components of airlines. Integer programming methods were utilized for decades to find solutions that offer a good balance between cost and operational efficiency. However, solving crew pairing problems is still computationally challenging for large-scale schedules. In this project we utilize deep learning based methods that leverage previous optimization results to predict solutions to new problems, increasing the solution speed and quality for a wide variety of airline crew pairing problems.
