Research on Cold Chain Logistics Route Optimization Based on Improved Genetic Algorithm

Abstract

With the rapid growth in cold chain demand, cold chain logistics has become increasingly important in modern supply chain systems. However, in practical distribution processes, route optimization becomes more complex due to temperature control requirements, product perishability, and multiple operational constraints. This paper addresses the routing optimization problem in cold chain logistics distribution by developing a mathematical model with the objective of minimizing total distribution cost, comprehensively considering vehicle fixed costs, transportation costs, refrigeration costs, and cargo deterioration costs. On this basis, an improved genetic algorithm is designed to solve the model. The initial population is generated using a nearest-neighbor priority strategy, and a local search mechanism is incorporated to enhance the algorithm’s search capability and convergence performance. A small-scale case is used for simulation analysis. The results show that the proposed method can obtain reasonable distribution routes under vehicle capacity constraints, and exhibits good convergence and stability, thereby verifying the effectiveness of the model and algorithm.