摘要：Today, the editor will introduce the literature review (2) ofAn integrated group fuzzy inference and best–worst method for supplie
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“颜读（28）：精读期刊论文《An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains》文献综述（2）”
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"Yan Du (28): Careful reading of the journal paper ‘An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains’ Literature review（2）"
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今天小编将从思维导图、精读内容、知识补充三个板块为大家带来《An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains》文献综述（2）的介绍。
Today, the editor will introduce the literature review (2) of An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains from three sections: mind mapping, in-depth content reading, and supplementary knowledge.
一、思维导图（Mind Mapping）
二、精读内容（Conduct in-depth reading of the material）
（1）供应商选择方法（Supplier selection methods）
本文在这一节主要回顾了已有的多准则决策方法（MCDM）在供应商选择中的应用，并指出了方法的发展趋势。在供应链管理中，MCDM方法经常用于选择供应商。由于其适应性和使用简单性，MCDM方法在解决涉及多个标准的绿色和社会责任供应链问题方面越来越受欢迎。层次分析法（AHP）是一种广为接受的MCDM技术，用于解决与评估和选择供应商相关的问题。近年来，随着最优-最劣法（BWM）的出现，许多研究者将BWM应用于供应商的评价和选择，一些研究者也将BWM与其他方法相结合用于此目的。
This section mainly reviews the existing applications of Multi-Criteria Decision-Making (MCDM) methods in supplier selection and points out the development trends of these methods. In supply chain management, MCDM methods are frequently employed for supplier selection. Due to their adaptability and ease of use, MCDM methods have become increasingly popular in addressing green and socially responsible supply chain issues that involve multiple criteria. The Analytic Hierarchy Process (AHP) is a widely accepted MCDM technique used to tackle problems related to the evaluation and selection of suppliers. In recent years, with the emergence of the Best-Worst Method (BWM), many researchers have applied BWM to supplier evaluation and selection, and some have also combined BWM with other methods for this purpose.
（2）模糊推理系统（fuzzy inference system）
本文在这一节介绍了模糊推理系统在供应商评价中的作用和方法选择。首先模糊推理系统是一种融合了专家系统技术和模糊逻辑的非线性系统，它由若干个从专家意见中获得的模糊IF-THEN规则组成，这些规则有效地模仿了人类的推理过程。其次常见模糊逻辑建模可以分为Mamdani模型和Takagi–Sugeno-Kang模型。Mamdani模型后件（输出部分）为模糊集合，可读性强、易于理解，更适合需要表达解释性的场景。Takagi–Sugeno-Kang模型后件为线性函数，更偏向精确计算，但可解释性较弱。本文认为Mamdani FIS更适合供应商选择问题，因为其结果表达更直观。最后文献表明FIS已经与多种MCDM方法结合用于交通运输、项目组合选择、医疗保健等。在供应商选择领域，已有多个案例将BWM与FIS结合。这些研究证明了FIS在处理复杂供应商选择问题上的有效性。
This section of the article introduces the role and method selection of fuzzy inference systems (FIS) in supplier evaluation. Firstly, a fuzzy inference system is a nonlinear system that integrates expert system technology and fuzzy logic. It consists of several fuzzy IF-THEN rules derived from expert opinions, which effectively mimic human reasoning processes. Secondly, common fuzzy logic modeling can be classified into the Mamdani model and the Takagi–Sugeno-Kang (TSK) model. The consequent (output part) of the Mamdani model is a fuzzy set, offering strong readability and ease of understanding, making it more suitable for scenarios requiring interpretability. In contrast, the TSK model features a linear function as its consequent, leaning more towards precise calculations but with weaker interpretability. This article argues that the Mamdani FIS is more appropriate for supplier selection problems due to its more intuitive result representation. Finally, the literature indicates that FIS has been combined with various Multi-Criteria Decision-Making (MCDM) methods for applications in transportation, project portfolio selection, healthcare, and other fields. In the realm of supplier selection, multiple cases have demonstrated the integration of BWM with FIS. These studies validate the effectiveness of FIS in addressing complex supplier selection problems.
三、知识补充（Supplementary Knowledge）
TOPSIS即逼近理想解排序法，由Hwang和Yoon (1981)提出，是一种经典的多准则决策方法（MCDM）。它的基本思想是：最优解应该最接近理想解，同时最远离负理想解。通过计算候选方案与理想解和负理想解的距离，得到每个方案的相对接近度，然后进行排序。TOPSIS的主要步骤包括：①构建决策矩阵；②标准化处理；③加权标准化矩阵；④确定理想解与负理想解；⑤计算与理想解、负理想解的距离；⑥计算相对接近度；⑦根据相对接近度的大小排序与选择最优方案。
TOPSIS, namely the Technique for Order Preference by Similarity to Ideal Solution, proposed by Hwang and Yoon in 1981, is a classic Multi-Criteria Decision-Making (MCDM) method. Its fundamental idea is that the optimal solution should be closest to the ideal solution while being farthest from the negative ideal solution. By calculating the distances between candidate alternatives and both the ideal and negative ideal solutions, the relative closeness of each alternative is obtained, followed by ranking them accordingly. The main steps of TOPSIS include: constructing a decision matrix; normalizing the data; weighting the normalized matrix; determining the ideal and negative ideal solutions; calculating the distances to the ideal and negative ideal solutions; computing the relative closeness; and ranking and selecting the optimal alternative based on the magnitude of the relative closeness.
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翻译：文心一言
参考资料：ChatGPT
参考文献：Tavana M, Sorooshian S, Mina H. An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains [J]. Annals of Operations Research, 2024, 342(1): 803-844.
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来源：LearningYard学苑
