Learning crew scheduling constraints from historical schedules

For most airlines, there are numerous policies, agreements and regulations that govern the workload of airline crew. Although some constraints are formally documented, there are many others based on established practice and tacit understanding. Consequently, the task of developing a formal representation of the constraints that govern the working conditions of an airline’s crew requires extensive time and effort involving interviews with the airline’s crew schedulers and detailed analysis of historical schedules. We have developed a system that infers crew scheduling constraints from historical crew schedules with the assistance of a domain expert. This system implements the ComCon algorithm developed to learn constraints that prescribe the limits of certain aspects of crew schedules. The algorithm induces complex multivariate constraints based on a set of user provided templates that outline the general structure of important constraints. The results of an evaluation conducted with crew schedules from two commercial airlines show that the system is capable of learning the majority of the minimum rest constraints.

► We present a system that learns crew scheduling constraints from historical schedules with the assistance of a domain expert. ► It uses a novel machine learning algorithm that learns constraints that prescribe the limits of certain aspects of crew schedules. ► Evaluation showed that the algorithm can infer majority of minimum rest constraints.