In the process of agricultural product distribution, there are problems such as low distribution efficiency, high cost, and high carbon emissions. This paper combines historical enterprise logistics data and real-time traffic data to establish a genetic algorithm-back propagation (GA-BP) neural network model. It optimises the distribution route of for agricultural products based on multiple targets of: time, cost, and carbon emissions. Compared with empirical methods, greedy algorithms and nearest neighbour algorithms, the distribution efficiency is improved by 22%, logistics cost optimisation rate up to 55%, and carbon emissions are significantly reduced, which further verifies the effectiveness of green logistics.
Wu et al. (Thu,) studied this question.