Environmental protection and intelligence have become the inevitable development trend of future transportation. Connected and automated vehicles (CAVs) are expected to be applied in the near future. In this context, how to schedule CAVs to meet customer demands with carbon emissions minimization has become a new green vehicle routing problem (VRP). Due to the fact that carbon emissions are tremendously influenced by vehicle speed, this paper considers vehicle speed as a decision variable in the above low-carbon VRP for CAVs. In addition, the differentiation on speed limits in each time period and each type of road are also taken into account. This study formulates a nonlinear mixed-integer programming model for this problem. The outer-approximate method is used to linearize the proposed model. Moreover, a hybrid particle swarm optimization (HPSO) algorithm is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method. Some implications are also drawn out for reducing carbon emissions in logistics activities.

环境保护和智能化已成为未来交通运输的必然发展趋势。互联汽车和自动驾驶汽车（CAV）有望在不久的将来应用。在这种情况下，如何通过最小化碳排放来安排CAV以满足客户需求已成为一个新的绿色车辆路径问题（VRP）。由于碳排放量受到车速的极大影响，因此本文将车速作为上述CAV低碳VRP的决策变量。此外，还应考虑每个时间段和每种道路的限速差异。本研究针对此问题制定了非线性混合整数规划模型。外部近似方法用于线性化所提出的模型。此外，为了解决这个问题，提出了一种混合粒子群优化算法。进行了广泛的数值实验，以验证所提出的模型的有效性和所提出的求解方法的效率。还为减少物流活动中的碳排放量提出了一些建议。