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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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跨境电子商务的导航:优先考虑混合MCGDM的物流合作伙伴
1School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
一个新的模型,HD-CBDTOPSIS,有效地使用不确定的数据评估跨境物流提供商. 这种混合方法提高了复杂的电子商务供应链的决策能力.
科学领域:
- 运营研究
- 供应链管理
- 决策科学
背景情况:
- 全球电子商务的增长需要有效的跨境物流.
- 由于数据多样化和不确定性,对物流服务提供商 (LSP) 的评估是复杂的.
- 现有的多标准决策模型面临着异质和不确定的数据.
研究的目的:
- 提出HD-CBDTOPSIS,一个混合的多标准集团决策模型来评估跨境LSP.
- 在物流服务提供商评估中处理异质数据类型和不确定性.
- 为在动态的电子商务环境中选择最佳的LSP提供一个强大的框架.
主要方法:
- 开发了HD-CBDTOPSIS,集成了用于不确定性表示的正常云模型 (NCM).
- 引入了一种改进的多步向后云转换与采样替代 (IMBCT-SR) 用于数据集转换.
- 使用新的云巴塔卡里亚距离 (CBD) 来测量相似性,并通过变化系数 (CV) 权衡客观标准.
- 应用基于云的订单偏好技术 (TOPSIS) 来对LSP进行排名.
主要成果:
- 拟议的HD-CBDTOPSIS模型有效处理异质数据 (准确,间隔,数据集,语言术语).
- 与传统的距离测量方法相比, 云巴塔卡里亚距离 (CBD) 显示出更高的差别.
- 使用基于CBD的目标权重确保了可靠的标准重要性.
- 该模型成功地在不确定的多维条件下对替代LSP进行排名.
结论:
- HD-CBDTOPSIS提供了一种有效,灵活和可靠的解决方案来评估跨境LSP.
- 在复杂的决策场景中,NCM和CBD的整合解决了关键问题.
- 这种模式支持全球电子商务物流领域的知情决策.
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