Research on cost-optimal distribution route planning for fresh and live agricultural products from a low-carbon perspective

Abstract

Abstract With the continuous development of the domestic economy, the residents' demand for higher and higher quality of agricultural products has made the logistics distribution of fresh and live agricultural products face great challenges. In view of this, this study constructs a fresh and live agricultural products logistics distribution path model based on improved genetic algorithm. Firstly, a cold chain logistics distribution path model from a low carbon perspective is proposed, a local search algorithm (2-opt) is used to optimise two local elements, an initialisation counter count is set to 0 and a suitable maximum number of iterations Gen is set, and finally the route is simulated using improved genetic algorithm analysis, and the logistics distribution path was implemented. The results show that the improved GA algorithm can make the average population fitness closer to the maximum population fitness with fewer iterations, reflecting that the improved algorithm effectively improves the population fitness capability and has a stronger ability to search for optimal solutions; the improved GA obtains a total of $7989.47 for each cost, which is significantly better than the traditional GA algorithm. The improved GA algorithm proposed in this study can effectively optimize the objectives with the lowest total cost and the lowest carbon emission, which can take into account both economic and environmental benefits.