Multi-objective optimization on robust airline schedule recover problem by using evolutionary computation

In this paper, we propose a method of multi-objective optimization evolutionary computation by using evaluated preference genetic algorithm (EPGA). The method is applied to quickly solve a time-effective aircraft routing in response to the schedule disruption of short-haul flights and tried to optimize objective functions including flight connection, flight duty swap, total flight delay time, delayed flights and flights over 30 minutes delay. The proposed EPGA approach here is a novel genetic algorithm to effectively resolve multi-objective optimization problems; it can consider multiple objectives simultaneously and then explore the optimal solution. Traditionally, airline schedule disruption management problem is solved by operations research (OR) techniques, which always require a precise mathematical model. But in real-world airline operation environment, there are too many factors to be considered dynamically, and thus it is very difficult to define a precise mathematical model in time. In this research, we propose EPGA to deal with robust airline schedule recover problem (RASRP) which is easier to model the practical problems. Furthermore, this method is verified by real flight schedules of Taiwan domestic airlines. The results show that the high quality solutions can be obtained in a few minutes. Therefore, EPGA can be used as a fast decision support tool for practical complex airline operations.