Optimizing the transportation route of fresh food in cold chain logistics by improved genetic algorithms

Abstract

At present, fresh food logistics transportation in China is still in the primary stage of development, transportation costs are rising, and cold chain logistics path design is unreasonable. Therefore, the optimization and prediction of the cold chain transportation route of fresh food has become the focus of the research in this field. Based on the principle of genetic algorithm, this paper designs an improved genetic algorithm to solve the problem of urban cold chain transportation path. In order to optimize the distribution path and minimize the total cost, a cold chain transport model is established. Through the simulation coding and calculation of the model, the influence of genetic algorithm on the optimization of the cold chain transport path is explored to reduce the cost and price of cold chain logistics transport, improve the transport efficiency, and thus improve the economic benefits of enterprises in this field. Through experiments, the optimal solution of the example is obtained, and compared with the traditional algorithm, it is proved that all the paths obtained by the improved genetic algorithm conform to the model with capacity constraint and time window constraint, and there is an optimal path for the most energy saving. In conclusion, the transport path of cold chain logistics calculated by the improved genetic algorithm is more optimized than the traditional algorithm and greatly improves the transport efficiency.