Research On Vehicle Routing Optimization Of Logistics Distribution Based On Carbon Emission Target

With the personalized needs of customers and the improvement of the timeliness of products,logistics distribution services present a small number of batches and multiple frequencies of distribution.At the same time,it also brings a series of problems such as low fuel efficiency and high vehicle no-load rate.These problems are environmental issues.The increasingly severe display today is particularly prominent.China,as the world’s largest carbon emitter,is also under tremendous pressure to reduce emissions internationally.And due to the constraints of technical funds and other issues,it will not be possible to replace traditional fuel vehicles with new energy vehicles in the short term.Therefore,reducing the energy consumption and carbon emissions of the logistics industry has become a hot topic of current research.At present,the academia generally takes the lowest cost as the objective for the vehicle routing problem.In this context,in this context,the optimization of the route with the minimum carbon emissions generated during the distribution process as the goal is of great social significance.The main contents of this article include:(1)This article first describes the basic theory of vehicle routing problems,introduces the development process of vehicle routing problems,and sorts out the current research status of vehicle routing problems at home and abroad according to the type of research.(2)Aiming at the research theme of this paper,vehicle fuel consumption and carbon emissions are calculated using a comprehensive emission model,and the effects of deadweight,driving speed,and road traffic conditions on vehicle fuel consumption and carbon emissions are analyzed.Under the constraints of time window cost and timely speed change,a mathematical model with minimum carbon emissions as the goal was constructed,and the model was transformed based on this to obtain the traditional route optimization model with the smallest distribution cost.(3)This paper uses an improved genetic algorithm to optimize the established path optimization model.Aiming at the problems of slow convergence speed,poor local optimization ability,and easy precocity in genetic algorithms,this paper uses the elite retention method to combine ranking and increase the roulette selection operator generated by random numbers,and introduces a predator search strategy to dynamically adjust the cross The method of mutation probability is used to improve the traditional genetic algorithm to improve the local optimization ability and convergence speed of the algorithm,and to improve the optimization efficiency.Based on the established path optimization model and constraint conditions,a reasonable algorithm step is designed and the problem is solved.(4)According to the specific case of the B distribution center,the established vehicle route model with the least carbon emissions and the lowest cost is applied to this case,and the model is optimized and solved using the improved genetic algorithm and the original genetic algorithm to obtain Specific vehicle distribution routes and related data.The following conclusions are obtained through example analysis:(1)The improved genetic algorithm has significantly improved the convergence speed and local optimization ability;(2)The path with the lowest fuel change cost may not be the path with the lowest cost,and the optimal path models The cost gap of the path is mainly concentrated on the time window cost,with a difference of 43.5%;(3)The optimal path based on the minimum carbon emission has reduced carbon emissions by 8.21%,but the total cost has only increased by1.67%.