目的 优化在区域时变路网下的生鲜冷链配送路径，降低企业的配送成本，提高配送效率。方法 针对冷链配送中的时效性和温控管理要求，构建结合道路拥堵状况的区域时变车辆行驶函数，建立基于生鲜冷链配送成本的优化模型;研究采用改进的粒子群算法(PSO-GA)进行求解，并比较区域时变模型、时变模型、静态模型的优化结果。结果 在求解精度和效率方面，PSO-GA均显著优于粒子群算法(PSO)和遗传算法(GA);在总配送成本方面，PSO-GA比PSO降低2.0%，比GA降低4.2%;在碳排放成本方面，PSO-GA比PSO降低3.9%，比GA降低11.2%。结论 模型在区域复杂拥堵环境下成功降低配送成本和碳排放成本，能够较好地仿真现实道路交通配送现状，具有很好的实际意义。

The work aims to optimize the cold chain distribution path of fresh food under the regional time-varying road network, to reduce the distribution cost of enterprises and improve the distribution efficiency. According to the timeliness and temperature management requirements of cold chain distribution, the regional time-varying vehicle travel function combined with road congestion was constructed, and the optimization model based on cold chain distribution cost of fresh food was established. The improved particle swarm optimization algorithm (PSO-GA) was used to solve the problem, and the optimization results of regional time-varying model, time-varying model and static model were compared. PSO-GA was superior to particle swarm optimization (PSO) and genetic algorithm (GA) in both accuracy and efficiency. In terms of total distribution cost, PSO-GA was 2.0% lower than PSO and 4.2% lower than GA. In terms of carbon emissions, PSO-GA was 3.9% lower than PSO and 11.2% lower than GA. The model can successfully reduce the distribution cost and carbon emission cost in the complex regional congestion environment, and can better simulate the reality of road traffic distribution, which has a good practical significance.