Research On Irregular Flights Recovery With Uncertainty In Airport Capacity

Bad weather,air traffic control and other factors often lead to airport capacity decline even closure,which cause difficulty of flights being carried out as original plan.However,these events are unpredictable,so as to irregular flights.When these disturbance factors incur flight delay,traditional deterministic model has many deficiencies in irregular flight recovery problem.In order to cope with irregular flight recovery problem caused by uncertain conditions,two uncertain factors,including airport dynamic capacity and flight transit time,are introduced.Firstly,a large number of historical data generated by the civil aviation industry is used to establish flight information database.Key factors and index sets,which affect airport dynamic capacity,are analyzed according to airport historical data.The method of support vector machine(SVM)is applied to forecast airport dynamic capacity.Secondly,in granularity of flight,external and internal factors associated with flight transit time are analyzed as well.The method of Bayesian network structure learning and parameter learning is applied to forecast flight transit time.Then,starting with irregular flights caused by airport capacity decline,the comprehensive theoretical research of aircraft rerouting problem is conducted.Additionally,a mathematical programming model with the objection of minimizing the delay cost of the airline is established,and heuristic algorithm is utilized to optimize the problem.Compared with traditional deterministic model,with the implement of Receding Horizon Control,the improved model regards airport capacity and flight transit time to be dynamic.The model not only reduces recovery cost,but also enhances its robustness,which can satisfy daily operation requirements of airlines.