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深度和传统机器学习模型的比较,用于预测一个供应链管理的分配成本
Xiaomo Yu1, Ling Tang2, Long Long3
1Department of Logistics Management and Engineering, Nanning Normal University, Nanning, 530001, Guangxi, China.
Scientific reports
|October 15, 2024
概括
卷积神经网络 (CNN) 准确预测供应链管理的分销成本 (SCMDC). 这种深度学习方法在优化供应链运营方面,比传统的机器学习模型提供了更高的性能.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 战略供应链管理 (SCM) 对于组织的成功至关重要.
- 准确预测供应链管理分销成本 (SCMDC) 对于性能优化至关重要.
研究的目的:
- 评估和比较SCMDC各种机器学习算法的预测准确度.
- 确定最有效的深度学习模型用于SCMDC预测.
主要方法:
- 使用了四种机器学习算法:随机森林 (RF),支持矢量机 (SVM),多层感知器 (MLP) 和决策树 (DT).
- 使用深度学习,特别是卷积神经网络 (CNN),用于SCMDC预测.
- 分析了180,519个开源数据点的综合数据集.
主要成果:
- 与其他模型相比,卷积神经网络 (CNN) 模型在预测SCMDC方面表现出更高的准确性.
- 在测试数据集上,CNN在测试数据集上实现了0.528的RMSE和0.953的R2值.
- 由于自动特征学习,模式识别和稳定性,CNN卓越.
结论:
- 对于精确可靠的SCMDC预测,CNN是首选的算法.
- CNN的优势包括效率,可扩展性和最小的预处理需求.
- 这些发现支持CNN在需要快速响应和有限计算资源的场景中的应用.
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