Research On One-to-many Vehicle And Freight Matching Based On Credit Evaluation System

In China,road freight transport industry has always been an important part of the national economy.According to the data of the Ministry of Transport,China's total annual freight transport in 2020 will be 52.485 billion tons,of which the total road freight transport will be34.264 billion tons,accounting for 65%.With the increase of freight volume and the integrated development of Internet information technology and logistics industry,the situation of "small but scattered,chaotic and miscellaneous" in the freight market is gradually prominent,and the transformation from offline transaction to online exchange brings increased virtuality of transaction and opacity of information.Each platform faces the same conundrum: how do you rate the credit rating of the owners of cars or goods on the platform? How to screen out the car owners and shippers with high credit rating? The establishment of a sound credit evaluation system has become one of the most concerned links of all platforms.In summarized on the basis of previous literature,this paper classified the existing highway freight logistics platform,focus on the services provided by the highway freight transport information exchange platform,based on the fuzzy chromatography analysis indicates that the factors affecting car owner's credit rating,after using a hidden markov model based on historical data to predict the future a period of time the owner or the owner's credit rating,Screening out the car shippers with high credit rating.Ideally,car owners with high credit rating are willing to cooperate with the platform,so that the platform has a virtual and reliable transport capacity pool,and stable and reliable shippers to provide supplies,and twoway guarantee of vehicle and cargo matching rate.According to the actual background of "more cars and less goods",this paper establishes a one-to-many vehicle and goods matching model with the goal of minimizing the owner's cost,which is solved by the Tabu Search algorithm(TS algorithm)which has good performance in solving VRP problems.In A platform,for example,the selection of the owner and the owner of A certain period of time to chengdu to kunming in the data,on the one hand,contrast based on the stable under the credit evaluation system of TS algorithm of matching scheme and does not consider matching scheme of credit evaluation system,on the other hand,after excluding the low credit car owners of TS algorithm scheme and not the actual match scheme compared with TS algorithm to solve the problem,Conclusion: under the background of stable credit evaluation system,the matching scheme calculated by TS algorithm not only reduces the logistics cost,but also improves the matching rate of shippers,thus verifying the applicability of the credit rating evaluation system and the vehicle and cargo matching model.