摘要：Today, the editor will introduce the proposed approach of anintegrated group fuzzy inference and best–worst method for supplier se
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“颜读（31）：精读期刊论文《An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains》提出方法”
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"Yan Du (31): Careful reading of the journal paper ‘An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains’ Proposed approach"
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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》提出方法的介绍。
Today, the editor will introduce the proposed approach 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）研究背景（Research background）
在制造业中，采购成本占生产成本的大部分。为了实现可持续和循环经济(CE)的目标，同时应对工业4.0的技术变革，企业在选择供应商时，不仅要考虑传统的经济因素，还必须兼顾社会责任、循环标准和新兴技术能力。因此，供应商选择问题变得更复杂，需要新的决策方法。本文提出了一种新的可持续循环供应商评价方法，结合模糊群BWM和FIS。
In the manufacturing industry, procurement costs constitute a significant portion of production expenses. To achieve the goals of sustainable and circular economy (CE) while addressing the technological transformations of Industry 4.0, enterprises must consider not only traditional economic factors but also social responsibility, circularity criteria, and emerging technological capabilities when selecting suppliers. Consequently, the supplier selection problem has become more complex, necessitating innovative decision-making approaches. This paper proposes a novel sustainable circular supplier evaluation method that integrates the Fuzzy Group Best-Worst Method (BWM) with Fuzzy Inference System (FIS).
（2）方法总体框架（Overall framework of the proposed method）
阶段一：计算各准则下的供应商得分。
首先识别并确定经济、循环、社会、工业4.0四大类准则及其子准则。然后使用模糊群体BWM来确定子准则的权重。之后专家根据语言变量（如“重要”“非常重要”）对最佳和最差准则进行两两比较，生成模糊向量。接着通过优化模型计算出各子准则的权重，并检验一致性。最后专家再对供应商在各子准则上的表现打分，最终得出各供应商在每一类准则下的加权得分。
Phase 1: Calculation of Supplier Scores under Each Criterion. First, the economic, circular, social, and Industry 4.0 criteria (along with their sub-criteria) are identified and defined. Subsequently, the Fuzzy Group Best-Worst Method (BWM) is employed to determine the weights of these sub-criteria. Experts then conduct pairwise comparisons of the best and worst sub-criteria using linguistic variables (e.g., "important," "very important") to generate fuzzy vectors. Next, an optimization model is applied to compute the weights of each sub-criterion, followed by a consistency check. Finally, experts evaluate supplier performance on each sub-criterion, and weighted scores for suppliers under each criterion category are derived.
阶段二：利用模糊推理系统(FIS)计算最终得分。
首先明确输入变量和输出变量，输入变量为四大类准则的得分（经济、循环、社会、工业4.0），输出变量为供应商的最终综合得分。然后采用Mamdani模糊推理模型，设定输入输出的隶属函数。之后基于专家的“if–then”规则建立推理规则。最后将供应商在四个准则下的得分输入系统，得到最终得分并进行排序。
Phase 2: Final Score Calculation Using a Fuzzy Inference System (FIS). First, the input and output variables are defined: the input variables are the scores of suppliers under the four criterion categories (economic, circular, social, and Industry 4.0), while the output variable is the supplier’s final comprehensive score. Next, the Mamdani fuzzy inference model is adopted, with membership functions specified for both input and output variables. Subsequently, inference rules are established based on expert-derived "if–then" rules. Finally, the scores of suppliers across the four criteria are input into the system to generate their final scores, which are then used for ranking.
三、知识补充（Supplementary Knowledge）
前景理论由Kahneman和Tversky(1979)提出，用于解释人在风险与不确定性环境下的决策行为，被广泛应用于行为经济学、金融学和决策科学。核心思想是人类决策并非完全理性，而是受到心理偏好的影响，主要体现在参照依赖、损失规避、边际效用递减四个方面。参照依赖体现为人们不是根据绝对效用，而是基于某个“参考点”来判断得失；损失规避体现为损失带来的心理痛苦要大于同等收益带来的快乐，通常认为损失的影响约是收益的2倍；边际效用递减体现为收益或损失的边际影响逐渐减弱；概率加权体现为人们会高估小概率事件，低估大概率事件。
Prospect Theory, proposed by Kahneman and Tversky in 1979, is employed to explain human decision-making behaviors in environments involving risk and uncertainty. It has been widely applied in behavioral economics, finance, and decision sciences. The core idea is that human decision-making is not entirely rational but is influenced by psychological biases, which are primarily manifested in four aspects: reference dependence, loss aversion, diminishing marginal utility, and probability weighting. Reference dependence means that people judge gains and losses not based on absolute utility but relative to a certain "reference point"; loss aversion indicates that the psychological pain caused by losses outweighs the pleasure derived from equivalent gains, with the impact of losses typically considered to be about twice that of gains; diminishing marginal utility refers to the gradual weakening of the marginal impact of gains or losses; probability weighting means that people tend to overestimate low-probability events and underestimate high-probability events.
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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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