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

Yi-Hua Chen

doi:10.1186/s13677-020-00174-x

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.

Highlights

With the improvement of people’s living standards, the structure and nutritional value of foods have received increasing attention
Fresh food is difficult to keep fresh at room temperature, so it needs to be distributed from the Correspondence: cy89aa@163.com School of software, South China Normal University, Nanhai 528225, Guangdong, China origin to the consumer terminal through cold chain logistics [1]
This paper is aimed at the problem of the real-time route optimization of cold chain logistics distribution vehicles, uses cloud computing processing methods for analysis, establishes an application service that is consistent with the actual distribution of cold chain logistics vehicles, builds an optimization model that meets the constraints, and provides planning and vehicle scheduling for cold chain logistics to provide support for optimization

Summary

Introduction

With the improvement of people’s living standards, the structure and nutritional value of foods have received increasing attention. The application of big data and cloud computing in logistics is based on the integration of logistics resources and capabilities, dynamic on-demand services, and integrated logistics service requirements [4]. Cloud computing can quickly search for real-time and dynamic information in a short period of time, can transfer the latest vehicle transportation, storage status, real-time routes and other information to logistics centres and vehicles, and obtain the calculation results in a short time, improving the efficiency of distribution and increasing economic benefits. Real-time route optimization services for cold chain logistics distribution vehicles based on cloud computing are used to realize distribution resources, distribution capabilities, and distribution knowledge sharing and on-demand use, improve the utilization rate of distribution resources, meet the personalized needs of users for services, and promote energy conservation and emissions reduction to achieve green and low-carbon manufacturing

Objectives

Methods