Research On Container Transport Network Routing Rationalization In The Entire Multimodal Transport

In the "12th five-year" program, it is clearly put requirements of "advancing multimodal transport of goods, improving container transportation system, improving the transportation efficiency and the level in the container multimodal transport" and so on. We can see that the development of container intermodal transportation has risen up to a national strategy. Its significance and importance are self-evident.Container intennodal transportation is the development and innovation of the international transportation organization technology, and it is becoming more and more important. Meanwhile, container transportation business is growing rapidly by the rapid development of global economy. Therefore, trying to take the advantage of container multimodal transportation and reasonably optimizing multimodal container transportation network entire route can realize "door to door" seamless links and effectively improve the efficiency of transportation of container multimodal transportation, so that reduce logistics cost and improve the service level and quality of the container multimodal transportation.This paper focuses on studying the transport network in container multimodal transport, and expounding elements of the multimodal container transport network system and operation mechanism. With the existing problems, it is put forward the essential rationalization countermeasures. Based on the theory of network system and graph theory, multimodal transportation route network model is established by container terminals standing for node and the route standing for arc. It is full of consideration in many affecting factors and subject to transportation time, interchange capacity and interchange times and other constraint conditions. And taking generalized cost minimum for target function in the entire of multimodal transportation as the target function to construct container multimodal network path rationalization mathematical model, and using the improved genetic algorithm to solve the optimization model and using Matlab simulation calculation to analyzes the calculation example. Finally, summarizes the basic work has been done, and presents the further research and study.