Ma Xingyu, Wang Chuanxu
School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel). 2025 Aug 19;27(8):876. doi: 10.3390/e27080876.
As global e-commerce expands, efficient cross-border logistics services have become essential. To support the evaluation of logistics service providers (LSPs), we propose (Technique for Order Preference by Similarity to Ideal Solution with heterogeneous data and cloud Bhattacharyya distance), a hybrid multi-criteria group decision-making (MCGDM) model designed to handle complex, uncertain data. Our criteria system integrates traditional supplier evaluation with cross-border e-commerce characteristics, using heterogeneous data types-including exact numbers, intervals, digital datasets, multi-granularity linguistic terms, and linguistic expressions. These are unified using normal cloud models (NCMs), ensuring uncertainty is consistently represented. A novel algorithm, improved multi-step backward cloud transformation with sampling replacement (IMBCT-SR), is developed for converting dataset-type indicators into cloud models. We also introduce a new similarity measure, the Cloud Bhattacharyya Distance (CBD), which shows superior discrimination ability compared to traditional distances. Using the coefficient of variation (CV) based on CBD, we objectively determine criteria weights. A cloud-based TOPSIS approach is then applied to rank alternative LSPs, with all variables modeled using NCMs to ensure consistent uncertainty representation. An application case and comparative experiments demonstrate that HD-CBDTOPSIS is an effective, flexible, and robust tool for evaluating cross-border LSPs under uncertain and multi-dimensional conditions.
随着全球电子商务的扩张，高效的跨境物流服务变得至关重要。为了支持对物流服务提供商（LSP）的评估，我们提出了HD-CBDTOPSIS（基于异构数据和云巴氏距离的理想解相似排序法），这是一种混合多准则群体决策（MCGDM）模型，旨在处理复杂、不确定的数据。我们的准则体系将传统供应商评估与跨境电子商务特征相结合，使用异构数据类型，包括精确数字、区间、数字数据集、多粒度语言术语和语言表达式。这些数据通过正态云模型（NCM）进行统一，确保不确定性得到一致表示。我们开发了一种新算法，即带采样替换的改进多步逆向云变换（IMBCT-SR），用于将数据集类型指标转换为云模型。我们还引入了一种新的相似性度量，即云巴氏距离（CBD），它与传统距离相比具有更强的区分能力。利用基于CBD的变异系数（CV），我们客观地确定准则权重。然后应用基于云的TOPSIS方法对备选LSP进行排名，所有变量均使用NCM进行建模，以确保不确定性表示的一致性。一个应用案例和对比实验表明，HD-CBDTOPSIS是在不确定和多维度条件下评估跨境LSP的有效、灵活且稳健的工具。
