考虑多任务的区域冷链多式联运路径研究

针对冷链物流运输成本高、时效性强、碳排放多的特点，集成考虑多任务、碳排放、客户满意度、运输弧容量约束因素，构建了以总成本最小、客户满意度最高为目标的冷链多式联运路径优化模型并采用遗传模拟退火算法进行求解。以南宁到哈尔滨的中国区域多式联运网络为案例，进行联运方案的决策，并对节点城市平均气温进行灵敏度分析。模型求解结果表明：所提算法相较于传统遗传算法具有更快的收敛速度和更高的精度，可有效求解该模型，三项运输任务的总满意度分别为0.53，0.86，0.75，生鲜种类、客户时效要求、运输弧容量均会对联运方案产生影响。灵敏度分析结果表明:随着城市平均气温的上升，客户满意度呈下降趋势。本研究结果可为不同运输情景下的冷链多式联运路径选择提供一定的参考依据。

According to the characteristics of high transportation cost, strong timeliness and high carbon emissions of cold chain logistics transportation, considering the constraints of multi task, carbon emissions, customer satisfaction and transportation arc capacity, a cold chain multimodal transportation path optimization model with the minimum total cost and the highest customer satisfaction is constructed and solved by genetic simulated annealing algorithm. Taking the China regional multimodal transport network from Nanning to Harbin as an example, the paper makes a decision on the intermodal transport scheme, and analyzes the sensitivity of the average temperature of node cities. The results show that the proposed algorithm has faster convergence speed and higher accuracy than the traditional genetic algorithm, and can effectively solve the model. The total satisfaction of the three transportation tasks is 0.53, 0.86 and 0.75, respectively. The types of fresh food, customer timeliness requirements and transportation arc capacity will have an impact on the intermodal scheme. The sensitivity analysis results show that: with the increase of urban average temperature, customer satisfaction shows a downward trend. The results of this study can provide some reference for the cold chain multimodal transport route selection under different transportation scenarios.