关键词: 神经网络, 非满载车辆, 线路优化

Abstract: In cargo circulation process, the current epidemic of vehicle scheduling is based on simple neural network model design, and thus make transportation cost waste. To this end, this paper proposes a non-full loads vehicle route optimization mining model based on improved neural network to solve the problem of non-full loads vehicle scheduling optimization in the process of transportation. Improved model by weighting the non-full loads vehicle length of time domain and the airspace of probability, constraining the neural network on steady state, building the non-full loads vehicle starting point and end point function equation improved algorithm distribution model, weighting time window of new model, to generate the distribution model of time window weighting and based on the new model, merges the improved neural network non-full loads mining vehicle model. The simulation results show that the mining model compared with the traditional calculation method of the neural network, can improve the profit of the non-full loads vehicle logistics, the effect is remarkable.

Key words: neural network, non-full loads vehicle, route optimization