A hybrid multiobjective genetic algorithm on optimizing aircraft schedule recovery problems under short-time response

This article presents a hybrid multiobjective genetic algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed algorithm extends from the original method of inequality-based multiobjective genetic algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.