Real-time decision support for airline schedule disruption management

Abstract

This study applied a method of inequality-based multiobjective genetic algorithm (MMGA) for real-time airline schedule disruption management in response to the schedule disruption of short-haul, quick turnaround flights with an environmental consideration. Empirical study based on a real-world airline flight schedule demonstrated that the proposed model can recover a disrupted schedule within about 3 CPU min which is more sufficient for real-time operation control. Consequently, it can be employed as a real-time decision supporting tool for practical complex airline operations to save operation cost, increase passengers’ convenience and reduce air pollution.