The airline crew scheduling problem involves determining schedules for airline crew members such that all the scheduled flights over a planning horizon (usually a month) are covered and the constraints are satisfied. Because of its complexity, this problem is usually solved sequentially in two main steps: the crew pairing followed by the crew assignment. However, finding a globally optimal solution via the sequential approach may be impossible because the decision domain of the crew assignment problem is reduced by decisions made in the pairing problem. This study considers the crew scheduling problem in a personalized context where each pilot and copilot requests a set of preferred flights and vacations each month. We propose a model that completely integrates the crew pairing and personalized assignment problems to generate personalized monthly schedules for a given set of pilots and copilots simultaneously in a single optimization step. The model keeps the pairings in the two problems as similar as possible so that the propagation of perturbations arising during the operation is reduced. We develop an integrated algorithm that combines alternating Lagrangian decomposition, column generation, and dynamic constraint aggregation. We investigate conditions for the convergence of the algorithm, and we conduct computational experiments on a set of real instances from a major US carrier. Our integrated approach produces significant cost savings and better satisfaction of crew preferences compared with the traditional sequential approach.
Published May 2018 , 18 pages
This cahier was revised in November 2018