运输模式对同时送取货绿色车辆路径问题中碳排放的影响

Impacts of Transportation Modes on Carbon Emissions in the Green Vehicle Routing Problem Synchronously Delivers and Pickups

摘要

为探究不同运输模式中各种因素对总成本和碳排放的影响，提出了同时配送和取货的绿色车辆路径规划问题模型. 该模型旨在确定环保路线，同时通过整合燃油消耗和碳排放等分项目的近似计算使总体成本最小化. 采用了一种两阶段技术来求解该问题. 对 Solomon 基准数据集进行重构，模型和算法的有效性与可行性已通过数值实验得到验证. 数据分析显示:在不同运输模式下，车辆速度、负载率、行驶距离和路线数量等因素对燃油消耗和碳排放的影响各不相同；使用异构车队能够通过减少所需运输车辆和路线的数量来增强集群的灵活性，从而大幅降低燃料消耗、碳排放以及整体运输成本；虽然通过运输模式选择可以使得碳排放大幅下降，但是，由于车辆数量的快速增加，还必须加强行政管理中的节能减排方案和实施措施的施行.

Abstract

To explore the impact of various factors in different transportation modes on total cost and carbon emissions, a green vehicle routing problem model synchronously delivers and pickups was proposed. This model aims to determine an environmentally friendly route while minimizing the overall cost by integrating approximate calculations of fuel consumption and carbon emissions. To solve this problem, a two-stage approach has been developed. Reconstruction and numerical experiments are conducted on the Solomon benchmark data set. The experiments indicate that the proposed model and algorithms are effective and feasible. Data analysis shows that under different transportation modes, factors such as vehicle speed, load rate, driving distance and the number of routes have different impacts on fuel consumption and carbon emissions; The use of heterogeneous fleets can enhance the flexibility of the cluster by reducing the number of required transport vehicles and routes, thereby significantly lowering fuel consumption, carbon emissions, and overall transportation costs; Although transport mode selection can significantly reduce carbon emissions, due to the rapid increase amount of vehicles, it is still necessary to strengthen the energy conservation and emission reduction plans and implementation measures in administrative management.

关键词

多种运输模式 / 能耗与碳排放 / 绿色车辆路径问题 / 同时配送与取货

Key words

multiple transportation modes / energy consumption and carbon emissions / green vehicle routing issues / synchronously delivers and pickups

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基金资助

江苏省高等学校基础科学(自然科学)研究重大资助项目(23KJA630001)