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增加物流的负载系数和用机器学习方法评估运输性能:来自汽车工业的案例
Raziye Kılıç Sarıgül1, Burak Erkayman2,3, Bilal Usanmaz4
1Department of Industrial Engineering, Faculty of Engineering, Ataturk University, Erzurum, Turkey. raziyekilic@atauni.edu.tr.
Scientific reports
|April 11, 2025
概括
在物流中优化车辆负载因子显著提高了效率. 使用机器学习的基于场景的方法使高性能货物从25.7%提高到98.4%.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 运营研究 运营研究
- 数据科学数据科学数据科学
背景情况:
- 低物流负载因子是运输行业的一个持续挑战.
- 车辆负载不足会降低整体效率并增加成本.
- 来自一家汽车公司的实际运输数据被用来解决这个问题.
研究的目的:
- 通过基于场景的方法提出一种有效的方法来提高物流效率.
- 通过使用机器学习算法来提高车辆加载性能.
- 分析优化负载因子对运输效率的影响.
主要方法:
- 开发了一种基于场景的方法,其中包括两个现实世界的场景.
- 机器学习算法,包括无监督聚类和监督分类,应用于未标记的货物数据.
- 使用集群和分类指标的平均成本来评估货物性能,用于监督学习.
主要成果:
- 基于场景的方法清楚地表明了负载因子的改善.
- 高性能货物从实际情况的25.7%增加到开发的场景中的98.4%.
- 机器学习有效地分组和分类了货物的性能.
结论:
- 优化物流负载因子可以实现更高效,更平衡的运输.
- 拟议的基于场景的方法显著提高了车辆的负载性能.
- 使用机器学习的数据驱动方法对于解决现实世界的物流问题是有效的.
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