LOCATION AND PATH OPTIMIZATION OF GREEN COLD CHAIN LOGISTICS BASED ON IMPROVED GENETIC ALGORITHM FROM THE PERSPECTIVE OF LOW CARBON AND ENVIRONMENTAL PROTECTION

Aiming at the distribution efficiency and cost of cold chain logistics of fresh products, a solution to the location-path optimization problem of green cold chain logistics using an improved genetic algorithm from the perspective of low-carbon environmental protection is proposed. First, considering transportation costs, fixed costs, refrigeration costs, and time penalty costs, increase carbon emission costs and cargo damage costs to build a cold chain logistics model. Then, combining the specific model of the optimization problem to improve the genetic algorithm, including the coding method, genetic operator and fitness function, etc., the linear adaptive cross-mutation strategy is introduced to dynamically adjust the genetic operator. Finally, the improved genetic algorithm is used to solve the cold chain logistics optimization problem to effectively reduce carbon emissions and cost in the cold chain distribution process. Using actual cold chain logistics and distribution data, the proposed method is verified based on the Matlab platform. The results show that compared with other methods, the proposed method accelerates the convergence speed, reduces the distribution cost, and realizes a low-carbon economy.