Intelligent algorithms for cold chain logistics distribution optimization based on big data cloud computing analysis

Abstract In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of this paper is to study the intelligent algorithm of cold chain logistics distribution optimization based on big data cloud computing analysis. Based on the constituent elements of the cold chain distribution problem and using cloud computing technology to obtain real-time traffic information in the transportation system through a unified access interface, this article analyses the distribution time and cost of refrigerated vehicles, thereby establishing a cold chain distribution vehicle path optimization model. By analysing the parallel programming mode of cloud computing, the parallel design and analysis of a coarse-grained genetic algorithm are used to solve the simulation model of the established optimization model. The experimental results show that the method of optimizing cold chain logistics vehicle routing using cloud computing is effective. When comparing 1, 2, 4, and 8 processors, the execution times are 19.89, 14.52, 8.12, and 6.41, respectively. It can be seen that the more processors there are, the shorter the calculation time.