Abstract: China’s national development and reform commission issued the “logistics industry adjustment and revitalization plan” in 2009 to support the development of agricultural product logistics and distribution centers. China’s agricultural product logistics and distribution have entered a stage of rapid development. With the rise of the sharing economy, logistics has become a bottleneck restricting the further development of agricultural product distribution. In order to realize the effective cooperation among the main body of agricultural product logistics distribution, improve the distribution efficiency and reduce the distribution cost, a logistics distribution optimization model based on the two-layer planning idea and genetic algorithm is proposed. A two-level programming model is constructed by combining qualitative and quantitative methods, theory and examples, and insertion and deletion operators are introduced to optimize the genetic algorithm. The research results show that the optimized genetic algorithm has a 54.55% increase in convergence speed, 1.08% in performance, and a 54.231% reduction in path length compared to the benchmark algorithm. It effectively improves the efficiency of path planning and saves the planning cost, and the final target value is reduced by 48.19%.

Na Wang, “Research on the Optimization Problem of Agricultural Product Logistics based on Genetic Algorithm under the Background of Sharing Economy” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131295

@article{Wang2022,
title = {Research on the Optimization Problem of Agricultural Product Logistics based on Genetic Algorithm under the Background of Sharing Economy},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131295},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131295},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Na Wang}
}