A ROUTE OPTIMIZATION MODEL BASED ON COLD CHAIN LOGISTICS DISTRIBUTION FOR FRESH AGRICULTURAL PRODUCTS FROM A LOW-CARBON PERSPECTIVE

Aiming at the carbon emissions caused by fuel consumption and refrigeration of distribution vehicles in the cold chain logistics of fresh agricultural products, this paper proposes a route optimization model based on logistics distribution for fresh agricultural products from a low-carbon perspective. Firstly, the distribution model is assumed based on actual logistics distribution of fresh agricultural products. Secondly, the fixed cost and transportation cost of vehicles, the loss cost and refrigeration cost of fresh agricultural products, the carbon emission cost and time penalty cost are considered to construct the distribution cost objective function. Then, the solution obtained by simulated annealing algorithm to solve objective function is used as the initial solution of ant colony algorithm to get the local optimal solution. Finally, the nearest neighbor search algorithm is combined to obtain the global optimal solution. In this paper, experimental research and comparative analysis are carried out under the actual fresh agricultural product logistics distribution data. The results show that the proposed algorithm is better than Ribonucleic Acid-ant Colony Algorithm (RACA), Quantum Particle Swarm Algorithm (QPSA) and Cycle Evolutionary Genetic Algorithm (CEGA). In the process of route optimization, the proposed algorithm tends to be stable when the number of iterations is 125, and its optimal cost is two hundred and thirty thousand RMB. Besides, RACA, QSPA and CEGA require 200, 255 and 300 iterations to achieve stability respectively. Correspondingly, their optimal costs also require two hundred and fifty thousand, two hundred and eighty thousand, and three hundred thousand RMB respectively.