Research On Optimization Of Multimodal Transport Path Considering Carbon Emissions In Uncertain Environment

Multimodal transportation effectively combines economic and reliable longdistance transportation and flexible and convenient road transportation to provide enterprises with efficient,low-carbon and reliable transportation services.The development of multimodal transport is an important part of deepening the construction of an integrated transportation system.It has important practical significance for the development of modern logistics,the improvement of the competitiveness of the national economy,and the cost reduction and efficiency increase of the real economy.With the increasing social and economic growth,low-carbon and green development has put forward new requirements for the transportation industry,which means that the realization of low-carbon transportation by multimodal transport is of great significance to the improvement of transportation quality.In addition,most domestic and foreign research focuses have focused on For the optimization of multimodal transport routes in a deterministic environment,some scholars have made useful explorations in the aspect of single uncertainty,but considering randomness or ambiguity alone cannot fully solve the problems caused by the advance of cargo transportation decisions and the complexity of the transportation process It is very meaningful to study the optimization of multimodal transport route considering carbon emissions under the double uncertain environment.The key to the optimization problem of multimodal transportation is to choose the transportation route and transportation mode reasonably in the intermodal transportation network,so that the cost,time and carbon emissions meet the requirements of both supply and demand.Aiming at the problem of low-carbon transportation and uncertainty of multimodal transport,this paper comprehensively analyzes the key influencing factors of cost and carbon emissions during transportation.With transportation costs and carbon emissions as the objective function,the mixed time window,path transportation capacity and A bi-objective optimization decision model is constructed under the constraints of node transshipment capacity.Random fuzzy variables are used to describe the dynamic cargo transportation process,which effectively quantifies the uncertainty of volume and time,and provides transportation organizers with a more realistic,accurate and flexible transportation organization scheme.A stochastic fuzzy chance constrained programming model is proposed to transform the objective function and constraints,and a hybrid intelligent algorithm solution model combining a stochastic fuzzy simulation algorithm and an improved genetic algorithm is designed.The example analysis verifies the reliability and effectiveness of the proposed optimization model and algorithm.