Flight Scheduling Optimization Based On Flight Delay

In recent years,the demand of civil aviation is increasing,and the problem of flight delay has been the primary factor restricting the development of civil aviation.In order to reduce flight delays caused by various reasons and take countermeasures,this paper proposes a flight delay combination prediction model based on flight delay data of an airline over the years,and uses the prediction results as support to optimize and adjust flight plans.The specific research contents are as follows:First,summarize the current situation of civil aviation development,and study the delay prediction and aircraft scheduling.Determine the definition and division of delay,analyze the main influencing factors of flight delay and the impact degree of different influencing factors on flight delay,and study the lack of research on delay prediction and flight adjustment.Then,in the face of flight delay problem,two combined prediction models are proposed by using ARIMA and BP neural network: weighted combined model and residual optimization combined model.Based on the prediction results,the weight of different influencing factors of flight delay is analyzed,and the time period with the most serious delay is selected for targeted research.Finally,under the condition that flight crew and aircraft routes are not changed,the dual objective function is established with the lowest delay cost and the highest robustness of aircraft plan,and the flight scheduling is adjusted in advance by optimizing flight departure time to absorb and sweep the delay.Combined with the actual flight operation data,Lingo software was used for empirical analysis,and the optimized results were compared with the actual flight operation from the perspective of flight cost and robustness.The calculation example shows that the total cost of flight is reduced by 4.31% and the robustness of flight plan is improved by 16.97% after optimization,which proves that the model can effectively improve the robustness of flight plan under the condition of cost control.