Optimization Of Multimodal Transport Paths Based On Low-carbon Transport In Uncertain Demand

Multimodal transport organically combines various kinds of transportation methods together to form a continuous and comprehensive integrated cargo transportation chain,which can exploit full advantages of different transportation methods and effectively reduce the costs of transportation transaction and social logistics operation.Therefore,the development of multimodal transport has become an inevitable requirement for the transformation and upgrading of the economic structure.It also becomes an inevitable trend to improve the overall logistics efficiency of the society.Besides,multimodal transportation is a combined transportation form of multiple transportation methods.The system constituted by this combination is still an uncertain planning.The transportation demand of the same transportation method in different time sections also fluctuates.In addition,with the increasing of global warming,human living environment is seriously threatened,and extreme weather occurs frequently,which also urges Chinese transportation industry to develop low-carbon transportation organization model.This measure can not only reduce the environmental pressure caused by global climate warming,but also effectively deal with China’s energy shortage problem and achieve long-term development of transportation industry.Considering the uncertainty of multimodal transport market demand and the impact of carbon emission control on the combination and route planning of multimodal transport,this paper proposes to study the problem of multimodal transport route optimization based on carbon emissions under uncertain demand.This article introduces the theoretical knowledge of multimodal transport and low-carbon transportation,and applies them in actual cargo transportation application.Considering the uncertain planning of transportation demand in the transportation process,the proposal constructs a multimodal transport path which is for planning this model.A genetic algorithm is designed to work out the model.Besides,examples are used to verify the effectiveness of the model.The main research contents of this article are as follows.(1)According to the actual transportation situation,for uncertain demands exist in the process of transportation,expressing with triangular fuzzy numbers in the uncertain planning,taking the carbon emissions of each mode of transportation into consideration,and introducing the carbon tax,converting carbon dioxide into carbon cost,discussing the impact of increasing carbon emissions on the choice of multimodal transport routes.(2)Constructing the objective function that minimizes the sum of costs between carbon dioxide emissions and transportation.The transportation cost includes the cost in the transportation process and the transit transportation cost,while the carbon emission cost includes the transportation carbon emission and the transit carbon emission.At the same time,establishing the uncertain model of constraints such as transportation time,transit and total carbon emission,both the objective function and constraints involve uncertain programming,using the chance constrained programming in fuzzy mathematics to solve uncertain programming with determinism,thus,converting the uncertain programming model to a clear deterministic model,and solving the model by designing genetic algorithm.Using examples to verify the effectiveness of the model,so as to provide a more scientific and reasonable quantitative reference for multimodal transport operators to formulate multimodal transport schemes.(3)To verify the effectiveness of the model and algorithm,designing and analyzing the multimodal transport of Guigang and Lianyungang as the background case.The result shows that under certain confidence level,the model can obtain the scheme with the minimum total cost.Meanwhile,comparing and analyzing the path selection scheme by setting different total carbon emissions,so as to provide a basis for decision makers to choose low-carbon transport road.