Comparison of Optimization Models for Cold-chain Logistics Distribution Routes of Aquatic Products: Analysis of Ant Colony Optimization and Simulated Annealing

This study aims to compare the performance of the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms in optimizing distribution routes for cold-chain logistics of aquatic products. First, a multi-objective optimization model is constructed with distribution cost, transportation time, and loss rate as core objectives, incorporating temperature control and time window constraints. Constraint integration is achieved through improved algorithm designs, such as the temperature deviation penalty term in ACO and the dynamic penalty function in SA. Second, based on a simulation scenario with 21 nodes (1 distribution center and 20 customer points), systematic comparative experiments are conducted on the two algorithms, focusing on core indicators including total driving distance, time window violation, total cost, and product loss rate. The results show that the SA algorithm performs better in comprehensive optimization indicators: the total cost is 1.87% lower than ACO, the product loss rate is reduced to 3.8%, the time window satisfaction rate is increased to 88%, and the temperature constraint violation rate is only 0.5%, making it more suitable for strict temperature control scenarios of long-distance and high-value aquatic products. The ACO algorithm, however, has an advantage in convergence speed (reaching the optimal solution in 27 iterations) and computational efficiency, suitable for dynamic route adjustment in small-to-medium distribution networks. The study suggests that future research can explore hybrid optimization strategies of the algorithms and validate the model's effectiveness in larger distribution networks, providing a quantitative basis for algorithm selection in cold-chain logistics enterprises.