关键词: 时隙二次分配, 航班波, 协同决策, 多目标优化, 改进非支配排序遗传算法

Abstract: With the rapid growth in air traffic flow, the air traffic network is becoming congested. Flight delays occur occasionally, which has become the primary issue faced by the civil aviation industry. When flight delays occur, collaborative slot secondary allocation can, to some extent, reduce the impact of delays on passenger travel, and significantly reduce the losses of airlines in terms of operation and revenue. The focus of this research is to readjust the scheduling between flights and slots, while minimizing the total delay costs of passengers and ensuring fairness in the allocation results. This article establishes a multi-objective slot secondary allocation model under flight wave operation, and designs an improved NSGA-II algorithm to solve it. Theoretically, it ensures the scientific and efficient optimization of flight schedule, and fills the gap of the research on slot secondary allocation under flight wave operation. Realistically, conducting this work provides an effective decision-making basis for the air management department and airlines, and reduces the loss of benefits for passengers and airlines.
This paper establishes a multi-objective cooperative slot secondary allocation model, which takes the minimization of the total delay costs of passenger as the efficiency objective, and the minimization of Gini coefficient as the fairness objective. The objective function takes into account the delay costs of arriving and transferring passengers, as well as the equalization of airline delay time. In terms of constraints, not only the operation characteristics of flight waves in the hub airport are considered, but also the maximum position shift (MPS) is introduced to reduce the workload of the airport controllers. In addition, this article develops an improved non dominated sorting genetic algorithm to solve the Pareto optimal solution set of the model. The main process of the algorithm incorporates duplicate individual control strategies and neighborhood search strategies. On the one hand, it eliminates repetitive individuals in the population after merging parents and children, accelerating the convergence speed of the algorithm. On the other hand, by expanding the search space through neighborhood search, Pareto solutions are enriched. Finally, citing the flight operation data of a large hub airport on a certain day, we construct three different scale examples for simulation experiments. We prove the superiority of the improved algorithm by calculating the Pareto solution set evaluation index.
From the solution results of the algorithm, it can be seen that all Pareto solutions satisfy the constraints, and the Pareto front is uniform and approaches the origin, indicating the applicability of the algorithm and the abundance and optimality of the solution set. The total delay costs of passengers obtained by the improved NSGA-II method is 14.2% less than the solution provided by the FCFS method, while the Gini coefficient represents a 46.3% decrease. This demonstrates that the improved NSGA-II algorithm may successfully reduce the total delay costs of passengers and guarantee the fairness of the average delay time amongst airlines. Moreover, the experiments of the series examples show that, for the minimum values or the average values of the objective functions, the results produced by the improved NSGA-II algorithm are better than those produced by the original NSGA-II algorithm. In terms of IGD index, the improved NSGA-II algorithm is 14%, 17%, and 31% smaller than the NSGA-II algorithm, respectively, which means that the improved NSGA-II algorithm has a better optimization performance. For SP index, the improved NSGA-II algorithm is smaller than the NSGA-II algorithm which can demonstrate that the improved NSGA-II algorithm has a higher-quality Pareto solution set. With regard to the average running time of the two algorithms, the improved NSGA-II algorithm does not take much more time, which proves that the time efficiency of the improved strategy is better.

Key words: slot secondary assignment, flight wave, collaborative decision-making, multi-objective optimization, improved non-dominated sorting genetic algorithm