A Mission-Oriented Aircraft Spare Parts Carried Consumption Prediction Method Based on XGBoost-GRA-DEMATEL

In order to improve the scientificity of the quantity guarantee of aircraft spare parts carried during the mission and fully consider all kinds of influencing factors in the mission, the XGBoost algorithm is adopted to predict the demand of aircraft spare parts carried. Firstly, various factors affecting aircraft spare parts consumption in different missions are analyzed, and a predictive feature system is established according to the principles of comprehensiveness, systematization, science and conciseness. Secondly, GRA, XGBoost, DEMATEL algorithm are used to analyze and screen the importance and relevance of features, and a simplified version of feature system is established. Thirdly, the grid search method is used to adjust parameters to improve the accuracy and efficiency of model prediction. Finally, through example analysis and comparative analysis with GBDT, SVM algorithms, it is verified that this method can reduce the prediction error and avoid over fitting in the case of limited sample data and man