车辆路线问题与时间窗口和碳排放:物流配送中的案例研究
Ping Lou1, Zikang Zhou2, Yuhang Zeng2
1School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China. louping@whut.edu.cn.
Environmental science and pollution research international
|February 7, 2024
概括
本研究引入了一种绿色物流方法,通过优化路线来减少车辆运输产生的碳排放. 它使用图形卷积网络和混合遗传算法来提高物流行业的效率和可持续性.
科学领域:
- 环境科学 环境科学
- 运营研究 运营研究
- 运输工程 运输工程
背景情况:
- 物流和运输部门是能源消耗和碳排放的重要贡献者.
- 车辆运输的碳足迹受到路线,道路状况,速度和速度变化的影响.
- 物流的可持续发展需要采用绿色物流实践.
研究的目的:
- 解决低碳车辆路由问题 (LCVRP),考虑高颗粒度,时间依赖的速度,速度波动,道路状况和时间窗口.
- 准确地建模和评估交通动态对碳排放的影响.
- 开发一种有效的算法来优化车辆路线,以尽量减少碳排放.
主要方法:
- 使用图形卷积网络 (GCN) 预测高颗粒度,时间依赖的流量速度.
- 结合自适应变量邻里搜索的混合基因算法被开发来解决LCVRP.
- 拟议的方法使用来自中国京州的现实物流和交通数据进行了验证.
主要成果:
- GCN有效地预测了高颗粒度的时间依赖的交通速度.
- 混合基因算法成功生成了低碳车辆路线.
- 案例研究证明了拟议方法在减少碳排放方面的实际有效性.
结论:
- 开发的方法为复杂的低碳车辆路线问题提供了可靠的解决方案.
- 将交通预测与先进的路由算法集成在一起,对于实现绿色物流至关重要.
- 这项研究提供了一个可行的策略,以减少物流行业对环境的影响.
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