Research On Logistics Distribution Path Optimization Of Electric Refrigerated Vehicles Based On Improved Genetic Algorithm

With the upgrading of residents’ consumption level and the rapid development of fresh e-commerce,people’s demand for cold-chain products is increasing day by day.While the cold-chain logistics industry ushers in broad development prospects,it also faces many problems,such as high logistics transportation costs,low efficiency of cold-chain distribution,and high product damage rate.In addition,cold-chain logistics,as an industry with high energy consumption and high carbon emissions,is increasingly polluting the environment.Under the background of carbon peak and carbon neutrality,the green upgrading and transformation of the cold chain logistics industry is urgent,and replacing the traditional fuel logistics vehicles with low-cost and low-pollution electric logistics vehicles will also become the future development trend.However,compared with the traditional fuel logistics vehicles,electric logistics vehicles still have a series of disadvantages,such as long charging time and insufficient range.Therefore,this thesis takes the cold-chain logistics distribution path optimization problem involving electric refrigerated vehicles as the research object,with the goal of minimizing the total cost,and the main work is as follows:(1)Summarize and analyze the existing research results and relevant basic theories to provide theoretical support for the follow-up research.(2)Based on the analysis of various costs combined with the characteristics of cold chain logistics and electric refrigerated vehicles,the optimization model with the goal of minimizing the total distribution cost is constructed by comprehensively considering the constraints of electricity,vehicle load and customer time window.(3)The improved genetic algorithm is selected to solve the objective function model.When designing the algorithm,the roulette wheel method,elite retention strategy and simulated annealing algorithm are introduced to ensure the diversity of the population and avoid premature convergence into local optimal solution.(4)Select A cold chain logistics distribution company for empirical analysis,and use MATLAB software to solve the optimization results for comparative analysis:(1)Compare the traditional genetic algorithm with the improved algorithm,and find that the improved algorithm has a stronger optimization ability,which can save the total cost and shorten the distribution distance.(2)By comparing the partial charging and full charging strategies of electric refrigerated vehicles,it is found that the partial charging strategy can be more flexible and can reduce the delivery time while reducing the total cost.The results show that the improved genetic algorithm designed in this thesis can reasonably plan the vehicle distribution path,improve the distribution efficiency,optimize the vehicle utilization rate,improve customer satisfaction,and reasonably optimize the resource allocation on the basis of ensuring the product quality,which has certain reference value for the path optimization of cold-chain logistics distribution enterprises.