Improved NSGA2 algorithm for disrupted departure flights recovery

To solve the problem of airline flight disruption caused by emergencies, this paper recovers the disrupted departure flight. A bi-objective optimization model for minimizing airline delay cost and passenger delay time is constructed. An adaptive non-dominated sorting genetic algorithm-Ⅱ based on dominant strengths (ANSGA2-DS) is designed. The novel crowding distance, the adaptive elitist retention technique, and the quick dominant sorting approach are the three enhanced operations that are given. The proposed algorithm is verified by the operation data of an airline in Fuzhou Changle International Airport. The experimental results reveal that, compared with the traditional first scheduling first serve method, the algorithm proposed in this paper can reduce the costs greatly. In contrast to the ε-constraint approach, the ε-constrained approach requires a longer solution time, and the resulting solution results are similar to those of the ε-constrained approach. Compared with the NSAG2 algorithm and the MOEAD algorithm, the algorithm proposed in this paper shows better performance. The proposed algorithm can solve the problem effectively and efficiently, and provide a basis for airlines to reach an optimized solution.