Workforce scheduling with large-scale mixed integer programming using column generation and 2D genetic algorithms: An application to airport ground staff scheduling

Abstract

Due to the large volume of transportation that takes place on the airport every day, airlines need to efficiently manage their employees and tasks to guarantee necessary operations are performed well and on time. The quality of the workforce scheduling has an important influence on both the task performance and costs of labor and operations that airlines will take. In this regard, this thesis proposes a mathematical modeling for airport ground staff scheduling with corresponding algorithms, including a 2D GA for shift planning with daily-wise shift formats, column generation for rostering and task dispatching, and integer programming for disruption management. The presented optimization-based workforce scheduling can replace the manual planning or serve as a reference for practitioners.