Research On Optimization Of Cold Chain Logistics Distribution Path Based On Time-Varying Road Network Considering Carbon Emissions

In recent years,with the continuous progress of electronic science and technology,online purchase of fresh products has become the mainstream way nowadays,and the scale of fresh cold chain order delivery has gradually expanded.To ensure the freshness of products and the timeliness of delivery,enterprises continue to expand the use of cold chain vehicles.However,on the one hand,due to the use of refrigeration equipment,the use of cold chain vehicles not only increases the economic cost,but also aggravates the emission of harmful gases such as CO₂,causing pollution to the environment;on the other hand,the speed of delivery vehicles on the road is time-varying and uncertain,and if the delivery time is too long,it may lead to a certain degree of damage to fresh products,thus reducing customer satisfaction.Therefore,in the real road environment,how to realize the low carbon distribution of cold chain logistics is an urgent problem to be solved.Firstly,based on theories such as cold chain logistics,low-carbon logistics,and time-varying road networks,the characteristics of the studied problem were clarified,and the congestion changes on real roads were analyzed.A calculation method for vehicle travel time under time-varying road networks was designed.The effects of factors such as vehicle speed,load,and refrigerant on vehicle fuel consumption and carbon emission costs were analyzed,and the composition of the total cost of cold chain logistics distribution was explored,Clarified the calculation method for various costs.Secondly,with the goal of minimizing overall cost,a cold chain logistics distribution path optimization model considering carbon emissions under time-varying road network conditions was constructed.Based on the characteristics of the problem,genetic algorithms are selected for solution.On the basis of traditional genetic algorithms,dynamic crossover and mutation mechanisms are designed,and large-scale neighborhood search algorithms are integrated to improve the local search ability of the algorithm and further improve the quality of the solution.Finally,three groups of data with different scale and different distribution types are selected,and the effectiveness of the algorithm is verified by model solving,and several sets of comparison experiments are designed:the improved genetic algorithm is compared with the traditional genetic algorithm,and the results show that the present algorithm solves better,converges faster,and has excellent improvement in all costs;the time-varying model is compared with two groups of"non-time-varying The comparison between the time-varying model and the two"non-time-varying"models not only proves the necessity of considering the time-varying road network,but also proves the advantages of the time-varying model in generating path distribution solutions;it is found that a reasonable choice of logistics vehicle travel time can effectively avoid traffic congestion,reduce vehicle distribution time and cost,and lower vehicle fuel cost,thus helping to realize low carbon distribution of cold chain logistics under time-varying road network.