目的 确保生鲜农产品在短时间内完成从配送中心到客户点的配送，提高配送效率，降低配送成本。方法 综合考虑温度变化对卸货过程中生鲜腐烂率的影响，运输过程和冷藏过程中的碳排放成本，以及违反时间窗的惩罚成本等因素，构建配送成本最小化模型，引入碳税机制定量分析碳排放成本，运用改进的自适应遗传算法进行求解，并对碳排放价格进行灵敏度分析。结果 优化后的总配送成本相较于优化前减少了3.8%，碳排放成本相较于优化前减少了27.8%，总配送时间相较于优化前减少了3.3%。结论 优化后的遗传算法在降低配送成本和碳排放成本上具有显著效果，可以通过合理控制碳排放价格等手段来降低配送成本和碳排放成本。

The work aims to ensure that fresh agricultural products can be distributed from the distribution center to the customer site in a short time, improve the distribution efficiency and reduce the distribution cost. With comprehensively consideration to the influence of temperature change on the fresh decay rate during unloading, the carbon emission cost during transportation and refrigeration, and the cost of violating the time window, a minimum distribution cost model was constructed. The carbon tax mechanism was introduced to analyze the carbon emission cost quantitatively. The optimization genetic algorithm was used to solve the problem to analyze the sensitivity of carbon emission price. The results showed that the optimized distribution cost was reduced by 3.8%. Compared with that before optimization, the carbon emission cost was reduced by 27.8%. The delivery time was 3.3% shorter than that before optimization. The optimized genetic algorithm is effective in reducing distribution cost and carbon emission cost. Distribution cost and carbon emission cost can be reduced through reasonable control of carbon emission price and other means.