A Study On The Path Modeling Of Low-Carbon Multimodal Transport Considering Time Window

According to the statistics of total freight traffic given by the National Bureau of Statistics of China,China's freight traffic is increasing year after year,and the total freight traffic is huge.A further Look into the freight traffic by all means of transportation shows that the annual freight traffic of the highways accounted for three quarters of all means of transportation,and there are large imbalances of freight traffic by different means of transportation.Although highway transportation can be fast and convenient,it also brings heavy pollution to the environment and is detrimental to the promotion of environmental progress.Therefore,other efficient and energy-saving transportation modes should be developed.Multimodal transport can make full use of the advantages of various means of transport to achieve the purpose of high efficiency and energy saving.Starting by the improvement of transportation efficiency and making full use of the advantages of various modes of transportation,this thesis studies the issue of multi-modal transportation path selection with time window,establishes a definitive path planning model,comprehensively takes the transportation cost,customer satisfaction and carbon emissions as the objective function of the model,and adjusts the proportion of each objective function by using weight coefficients,so that the decision preference of different decision-makers can be satisfied and the flexibility of the model improved;the time window of service and the ability of service are fully considered in the constraint;a reasonable example is generated according to the nature of the problem,and the algorithm is written by Python to solve the example,which verifies the validity of the model;the sensitivity analysis of the key variables of the model is carried out,and it is found that service time have considerable influence to the results.For the multimodal transport path problem with uncertain service time,the service time is represented by random variables.It is difficult to use stochastic model to solve problems.The neural network algorithm has excellent learning and self-adaptive ability,and has good tolerance for uncertainties.Therefore,the intelligent optimization algorithm neural network is used to solve the problem.The example analysis shows that the neural network can give a robust solution at a certain level of confidence,which is of great significance for guiding practical decision-making;Then,the stochastic model is transformed into the chance constrained model,and the sample simulation method is used to solve the model.Firstly,the model is transformed into the deterministic model,and the sample data is substituted into the deterministic model.Through numerical calculation,it is found that the order with sensitive time window will sacrifice cost to offset uncertainties.