Research On The Optimization Of Multi-Point Loading And Unloading Routing In Road Transportation Based On Mixed Time Window

Road transportation is one of the main modes in modern transportation,and plays an important role in the field of logistics and transportation.According to statistics,road transport accounts for about 80%in all modes of transport in China.Reviewing the history and current situation of China's road transportation,it can be found that although the costs is not high compared to abroad,the entire logistics system is inefficient.So improving the efficiency of decentralized transportation and reducing the no-load rate are significant for our society.With the development of Internet technology,some new models have been proposed in recent years,such as the transmission of highway ports and the bus freight mode.And in this new background the traditional freight industry is facing the opportunity of upgrading.The advent of the Internet era makes the information exchange more timely.And the development of information transmission and data processing technology makes the vehicle positioning,scheduling,Unicom and other relevant technologies more reliable.Considering the reality,many small and medium-sized cargo transport may have consistent needs in the transport direction.Besides,general heavy goods generally meet the mixed conditions.So it's possible for an empty return or non-full load vehicle to match needs under some certain conditions along the multi-point loading and unloading transport to replace the traditional single point of loading and unloading.This method will obviously improve the income of driver,but also more able to fully explore the capacity to reduce the no-load rate and improve the load rate of the situation.Therefore,the main research object of this study is the optimization problem of multi-point loading and unloading path in road transport.Based on the existing research results,the optimization model of single-vehicle multi-point loading and unloading path based on mixed time window is established.The corresponding two-stage algorithm is used to solve the optimization model,including the matching phase algorithm based on the improved particle swarm algorithm and the path optimization algorithm based on the improved genetic algorithm.Finally,the feasibility and validity of the model and algorithm are verified by an example.At the end of the study,the innovation and conclusions of the research are summarized in stages,and the direction and problems worthy of further study in this field are prospected.