Integration of Multimodal Logistics at Frankfurt Airport: An Engineering Perspective
Hu, Jinkun (2025)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025052515820
https://urn.fi/URN:NBN:fi:amk-2025052515820
Tiivistelmä
With the rapid development of the global economy, the demand for efficient and green transportation methods in the supply chain is increasing daily. Multimodal transport, as a logistics model that organically combines transportation modes such as air, road and railway, can provide a comprehensive transportation solution that is fast, low-cost and energy-saving. The continuous expansion of international freight volume and transportation distance has also promoted the development trend of multimodal transport integration. (Okyere, S., Yang, J., & Adams, C. A., 2022) Frankfurt Airport, as an important air cargo hub in Europe, has achieved remarkable results in multimodal transport integration. According to Fraport's statistics, the airport's cargo and mail throughput reached approximately 2.1 million tons in 2024, representing a year-on-year growth of 6.2%. (TIACA, 2025) This growth is attributed to the airport's well-developed intermodal infrastructure and the construction of air-rail intermodal channels, which have effectively enhanced the efficiency of cargo transfer and transportation capacity.
From an engineering perspective, the efficiency of multimodal transport can be further enhanced through optimization models and simulation techniques. Advanced algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are widely used in the path planning and scheduling optimization of multimodal transport networks. Studies show that compared with the traditional mode, the schemes adopting these optimization algorithms can reduce the total cost of the transportation system by approximately 4-5%, while significantly improving the transportation time and resource utilization rate. (Zhang, T., Cheng, J., & Zou, Y., 2024) Based on the actual operation data of Frankfurt Airport, this thesis deeply analyses the application effect of multimodal transport integration from the perspective of logistics engineering, providing a reference for hub airports to improve transportation efficiency and integration level.
From an engineering perspective, the efficiency of multimodal transport can be further enhanced through optimization models and simulation techniques. Advanced algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are widely used in the path planning and scheduling optimization of multimodal transport networks. Studies show that compared with the traditional mode, the schemes adopting these optimization algorithms can reduce the total cost of the transportation system by approximately 4-5%, while significantly improving the transportation time and resource utilization rate. (Zhang, T., Cheng, J., & Zou, Y., 2024) Based on the actual operation data of Frankfurt Airport, this thesis deeply analyses the application effect of multimodal transport integration from the perspective of logistics engineering, providing a reference for hub airports to improve transportation efficiency and integration level.