Research on the Cold Chain Logistics Delivery Path of Urban Fresh Products Under the Time Varying Road Network

In response to the high total cost and carbon emissions in cold chain logistics distribution in cities, this paper proposes a fresh product distribution problem that minimizes the total distribution cost, including carbon emissions costs, under a dynamic road network. At the same time, the impact of load on the power consumption rate of electric vehicles in distribution is considered, and a measured model for power consumption and carbon emissions based on load is introduced. A path optimization model for electric vehicles with time windows and capacity constraints is established, and an adaptive genetic algorithm is designed to solve it. The results show that the distribution scheme under variable speeds can reduce the total cost and carbon emissions costs by 5.83% and 28.52%, respectively. The adaptive genetic algorithm used in this paper has stronger convergence and optimization capabilities than traditional genetic algorithms. Therefore, the proposed model and algorithm can improve enterprise economic benefits while promoting energy conservation and emission reduction, and provide reference basis for decision-making in urban cold chain distribution paths.