Research on optimization of cold chain path for joint distribution of fresh food based on hybrid genetic annealing algorithm

As fresh food markets demand grows, optimizing fresh food cold chain logistics path through the hybrid genetic simulated annealing algorithm (HGSA) has become a focus for enterprises. A mathematical model was constructed, considering distribution cost, time window, and vehicle load constraints. The HGSA combines the global search capabilities of the genetic algorithm with the local search advantages of the simulated annealing algorithm. Case analysis showed the proposed algorithm significantly reduces total distribution cost and improves optimization efficiency compared to traditional methods. Algorithm convergence evaluation verified the effectiveness and stability of this method in complex system optimization. Although the research has achieved certain results, the model's universality and the algorithm's computational complexity need further study. Future work will focus on improving the model's generalization ability, exploring the algorithm's efficient scalability, and enhancing the model's adaptability to uncertainty factors.