Abstract: This paper studies the influence of real - time traffic conditions caused by frequent traffic congestion on the vehicle routing problem with the consideration of carbon emissions. The real-time traffic conditions are represented by the traffic state index, and the integer programming model is established with the aim of carbon emissions and the delivery time. The improved particle swarm optimization algorithm is designed to solve the model. Numerical examples show that the improved particle swarm optimization algorithm can find a satisfactory solution. The Pareto solution proves that the proposed method can reduce carbon emissions. With the increase of traffic state index, the reduction of carbon emissions is more obvious.

Key words: time-dependent vehicle routing problem, carbon emissions, particle swarm optimization

摘要： 研究了由常发性交通拥堵造成的实时交通状况变化对低碳车辆路径优化的影响。用道路交通状态指数表示城市实时交通状况，以低碳和配送时间最短为目标建立整数规划模型进行路径优化。设计了改进的粒子群算法进行求解，得到帕累托前沿解集。数值算例表明，改进的粒子群算法能有效找到满意解。通过帕累托解集可以证明该方法可以在牺牲少量配送时间的前提下减少碳排放量。随着交通状态指数的增大碳排放量的优化效果更加明显。

关键词: 时变车辆路径优化, 碳排放, 粒子群算法