Solving the airline crew recovery problem by a genetic algorithm with local improvement

Within the complex and dynamic environment of the airline industry, any disturbance to normal operations has dramatic impact, and usually imposes high additional costs. Because of irregular events during day-to-day operations, airline crew schedules are rarely operated as planned in practice. Therefore, disrupted schedules should be recovered with as small changes as possible. In this article, we propose a genetic algorithm (GA) based approach, in which disrupted flights are reassigned within an evolutionary process. Because of the slow convergence rate achieved by conventional GA, a special local improvement procedure is applied in this approach. Computational results are reported for several disruption scenarios on real-life instances from a medium-sized European airline.