Applications of Multi-objective Evolutionary Algorithm to Airline Disruption Management

Since domestic flights are mostly short-haul flights and the markets are very competitive, any minor perturbation of the schedules can result in a chain of events that can cause major disruptions throughout the system. When a disruption occurs on the day of operation, the first priority for the airline is then to restore the original flight schedule as soon as possible to minimize lost revenues and operational costs. In this paper, we adopt a method of using multi-objective evolutionary algorithm (MOEA) to deal with the disruptions management problem of Taiwan domestic flights by minimizing an objective function involving the total delay and swaps of the schedules. The MOEA approach, a method of combining the traditional genetic algorithm (TGA) with the multi-objective method, can consider the relation between the parameters and the objective spaces in the same time then explore the optimum solution. The algorithms are tested on real flight schedule obtained from a Taiwan domestic airline. The results show that the application is capable of presenting high quality solutions in a few second and therefore can be used as a real-time decision support tool by the airlines.