OPTIMIZATION MODEL OF COLD CHAIN LOGISTICS DELIVERY PATH BASED ON GENETIC ALGORITHM

This study is for optimizing the distribution path problem of cold chain logistics. This study proposes an improved genetic algorithm that introduces natural number coding, elite preservation strategy and adaptive cross -mutation strategy. A cold chain logistics distribution path optimization model is constructed, taking into account various costs, including customer demand, time window requirements, maximum mileage of refrigerated trucks, payload, and other constraints. To address the cold chain logistics distribution path, an improved genetic algorithm is utilized. This study designs experiments to test the performance of improved genetic algorithms and applies the model to an example for experimental analysis. The results show that the improved genetic algorithm has better performance in convergence and convergence speed. From the perspective of distribution cost, the optimization model based on the improved algorithm significantly reduces the total distribution cost compared with that before optimization. The above results show that this study effectively optimizes the cold chain logistics distribution route by improving the genetic algorithm and significantly reducing the total distribution cost. This study not only proves the effectiveness of elite preservation strategy and adaptive cross -variation strategy but also shows the importance of considering various costs and constraints comprehensively. This provides a valuable optimization tool for the cold chain logistics industry, helps to improve efficiency and reduce costs, and has important practical significance.