Study on Logistics Demand Forecasting Based on Improved Adaptive Genetic Algorithm and BP Neural Network

Guoyin YU

School of Management, Chongqing Jiaotong University, Chongqing 400074, CHINA

Abstract: Logistics demand forecasting is the basis of making logistics development policy. In order to improve the accuracy of logistics demand forecasting, an improved adaptive genetic algorithm (IAGA) was designed at first. Then a logistics forecasting method was established with improved adaptive genetic algorithm and BP neural network. It made the initial weights and threshold of BP neural network optimized by GA .Further, the IAGABP neural network model was constructed to forecast the logistics demand. The empirical results show that, the IAGABP algorithm can predict logistics demand more accurately.

Keywords: Adaptive genetic algorithm; BP neural network; Logistics demand forecasting