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This study proposes a multi-objective mixed integer linear programming (MOMILP) model for assigning a set of flights to different runways and determining their actual arrival and departure times. The proposed model envisages unique operation model of each runway (i.e., takeoff, landing, or mixed takeoff and landing). Further, interference in two flights between adjacent runways are also fully considered in this model. The work aims at reveal the optimal relationship between traffic stream characteristics, operation mode of each runway and flight scheduling to simultaneously minimizing flight delays and maximizing runway utilization. Since the problem of interest has a non-deterministic polynomial (NP-hard) complexity, a heuristic-based non-dominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto-optimal solutions in a reasonable amount of time, where coding structure and heuristic algorithm for producing initial population are defined. Finally, a real-world example is provided to compare the difference in quality between the proposed and traditional models, and reveal changes in trends between delay time of flights and idle time of the runways, which can verify the correctness of the model.

This paper proposes an optimization model for the integrated aircraft flight scheduling and routing problem, which allows a simultaneous determination of the departure time of each flight trip and assignment of a set of aircraft located at different airports to perform all flight trips. The proposed model envisages that each flight trip is covered by its own particular aircraft type or a larger airplane. Further, departure and arrival times of each flight trip are within a flexible time window in its aircraft's route and origin/destination airports, and the number of airplanes firstly distributed in the base airports is fully accounted for in the model. The model not only can effectively minimize weighted operation costs for the number of airplanes and the total idle time for adjacent flight trips covered by an aircraft, but also can maximize the number of transported passengers. This paper further presents a two-stage heuristic approach based on the ant colony optimization algorithm, which efficiently finds the most acceptable solutions. The above algorithm is used to generate a series of aircraft routes, and a polynomial algorithm is designed to obtain their feasible flight trip timetable. Finally, the model is applied to a case study to design the integrated aircraft flight scheduling and routing plan for a real airline in China. A comparative analysis of the conventional and proposed models proved the latter's feasibility.

The present research envisages a novel group decision making model to evaluate the operational risk of airports from four aspects of human, equipment, management and environment factors. The proposed model featured an integration of intuitionistic fuzzy set and set pair analysis. Due to the lack of the systematic data and quantitative analysis concerning the uncertainty of these indicators, an intuitionistic fuzzy set was used to characterize them, which converted them into the ternary connection numbers based on set pair analysis. A new distance based on the intuitionistic fuzzy set and set pair analysis was proposed to analyze the consistency degree of any two experts on the same airport operation risk, wherein the degree of contact determined both the uncertainty and certainty of each indicator, so as to obtain the ranking degree of the expert group on the operation risk of all airports. Moreover, the relationship between the value of these indicators and the threshold changes of the airport operation risk ranking was evaluated. This study could be used as an effective tool for transit authorities to rank the operational risk of different airports, by comprehensively considering the viewpoint deviation of different decision makers on the same scheme, and its uncertainty factors. The analysis of the case study comprising four airports in China showed that with an increase in the degree of contact, the operation risk value of the airport in Beijing remained the same that of Tianjin and Qinhuangdao decreased, and for Shijiazhuang gradually increased.