Research on Aircraft Delivery Forecasting and Supply-demand Matching Optimization Based on Machine Learning Algorithm

This paper proposes a joint optimization model based on deep learning for aircraft delivery forecasting and supply-demand matching. The model processes historical delivery data and market demand characteristics through DNN to predict aircraft delivery, and combines the optimization algorithm to tune production scheduling and resource allocation, thereby achieving accurate matching of supply and demand. Experimental simulation results show that the joint model based on deep learning is significantly better than traditional statistical methods in prediction accuracy. Specifically, the prediction error of the deep learning model is reduced by about 18%, and compared with the traditional prediction method based on time series, it can better capture the complex nonlinear relationship of market demand. In addition, in terms of supply and demand matching optimization, the model can effectively reduce resource waste, optimize production scheduling, and reduce inventory costs by about 12%. Compared with traditional optimization methods, the deep learning joint model performs better in convergence speed and optimization effect.