| Abstract |
China's cities have frequent logistics activities, large amount of information, many logistics nodes, small distribution volume, variety and high frequency, thus becoming a research hotspot in related fields. In recent years, the logistics demand of Chinese cities has increased dramatically. The traditional urban logistics organization model still faces the problems of cumbersome transfer levels, high resource and energy consumption, and high safety risks and hazards, which have seriously constrained the ecological construction, high-quality development and intelligent transformation of Chinese cities. Against this background, the innovative concept of “building a smart city based on smart logistics” is expected to fundamentally solve the above problems. Intelligent logistics makes use of big data, cloud computing, artificial intelligence and other intelligent technologies and methods to enhance the visualization, perceptibility and adjustability of the logistics system, so as to further improve its overall level of intelligence and automation, thus promoting the quality of China's logistics services. This study provides an in-depth analysis of the logistics network layout and optimization system in Chinese cities, and explores the concept of smart city construction based on intelligent logistics. By constructing a model with the objectives of minimizing total cost and maximizing system utility, and optimizing the node layout using genetic algorithm, the efficiency of the logistics system is significantly improved. The experimental results show that under the configuration of five primary nodes and ten secondary nodes, the total cost is 4 million￥, while under the configuration of six primary nodes and fifteen secondary nodes, the total cost rises to 6.5 million ￥. This shows that rational planning of logistics node layout can effectively reduce the overall cost of urban logistics system and improve service quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. |
