###### Abstract  
In the current era, the government consistently emphasizes the pursuit of high-quality development, as evidenced by the ongoing increase in the tertiary industry's GDP share. As a crucial component of the modern service sector, logistics plays a pivotal role in determining the operational efficiency and overall quality of the industrial economy. This study focuses on constructing a Chongqing logistics express demand prediction index system. It employs an improved BP neural network model to forecast the logistics express demand for Chongqing over the next five years. Given the limited express demand data sequence and the normalized characteristics of the data, the selected training method is the Bayesian regularization approach, with the LeCun Tanh function serving as the hidden layer activation function. Additionally, a genetic algorithm is designed to optimize the initial weights and thresholds of the BP neural network, thereby enhancing prediction accuracy and reducing the number of iterations. The experimental results of the improved GA-BP network are analyzed and compared, demonstrating that the improved BP neural network, utilizing GA optimization, can more reliably and accurately predict regional logistics express demand. According to the findings, the forecast indicates that the logistics express demand for Chongqing in 2026 will be 2,171,642,700 items. region logistics development; demand forecasting; logistics planning; neural network optimization; genetic algorithm +
Footnote †: journal: Applied Sciences  
Japanese scholar [PERSON] has proposed the \"third source of profit\" doctrine, which highlights that in modern society, as industrial operation systems become more comprehensive and market competition intensifies, there is a limit to expanding profits through industrial development alone. Effectively reducing logistics costs, which constitute a significant portion of the total cost, equates to increasing profits, thus representing the third source of profit. Logistics costs are mainly divided into three major categories, namely transport costs, warehousing costs, and management costs. The reduction of logistics costs is of great significance in enhancing industrial efficiency, optimizing supply chain management, and promoting economic development. [PERSON] and [PERSON] [1] observed that the cost challenges associated with e-commerce logistics were further exacerbated during the COVID-19 pandemic. To effectively control logistics costs, it is essential to enhance information exchange among suppliers, manufacturers, third-party logistics service providers, shipping companies, and other stakeholders involved in the distribution chain. Therefore, the forecasting of transaction demand and logistics costs has become increasingly critical. [PERSON] [2] proposed the implementation of a Lean Six Sigma (LSS) framework within the supply chain, exploring strategies to reduce logistics costs through practical case studies. The findings indicated that a primary reason for the rise in logistics costs was insufficient communication among different departments within the supply chain. Future research directions should focus on the development of a unified production plan and the enhancement of integrated functions, with logistics demand forecasting playing a vital role in the formulation of this production plan. [PERSON] and [PERSON] [3] introduced a framework for a Logistics 4.0 system grounded in the principles of Industry 4.0. In their article, Logistics 4.0 is defined as a logistics system designed to meet individual customer needs in a cost-neutral manner, leveraging digital technologies to facilitate this development in both industry and trade. Emerging trends such as mass customization and sustainability have increased the complexity and demand for logistics systems. Effectively managing this complexity necessitates the development of advanced planning and control instruments.