Abstract
With the continuous development of the economy, people’s lifestyle has changed greatly, online shopping has become a better choice for many people, and the express business volume is also increasing. Forecasting express business volume is of benefit to the healthy development of the logistics industry. Based on the data of China’s express business volume from 2015 to 2019, this paper uses the improved Particle Swarm Optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and forecasts China’s express business volume from 2020 to 2023. The results indicate that in the next few years, China’s express business volume will show a large growth trend, indicating that the express delivery industry still has a lot of room for development.
Highlights
Since the Internet has become widely available, people get accustomed to shopping online
E rapid growth of express business volume brings about the development of the whole logistics industry
The unreasonable allocation of various resources leads to the disorderly development of the logistics industry
Summary
Since the Internet has become widely available, people get accustomed to shopping online. Online shopping is convenient and time-saving, which has facilitated the development of China’s express business volume in recent years. E following content will do specific research on the growth of China’s express business volume. Erefore, predicting the express business volume will help related personnel to make scientific decisions, and promote the healthy development of the whole logistics industry. FGM (1, 1) model has been applied to predict solid waste treatment capacity, Journal of Mathematics volume data (100 million). According to the data from China’s express business from 2015-2019, this paper uses the improved particle swarm optimization algorithm to solve the fractional-order r of the FGM (1, 1) model and predicts the express business volume of China in the coming years through the FGM (1, 1) model. 2. Modeling Process of FGM (1, 1) Model and Particle Swarm Optimization Algorithm.
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