摘要：Today, the editor will introduce the case study (2) of anintegrated group fuzzy inference and best–worst method for supplier selec
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“颜读（37）：精读期刊论文《An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains》案例研究（2）”
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"Yan Du (37): Careful reading of the journal paper ‘An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains’ Case study (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》案例研究（2）的介绍。
Today, the editor will introduce the case study (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）方法应用流程（Method application process）
1.使用模糊群体BWM（Utilize fuzzy group BWM）
在第一阶段，使用模糊群BWM计算子标准的权重，最终确定每个标准的供应商得分。具体步骤如下：通过查阅文献和运用专家知识，从经济、循环、社会和工业4.0方面确定了评估供应商的次级标准；每个专家分别选出最优与最劣子准则，并进行成对比较；通过优化模型（CPLEX/GAMS）计算子准则权重，检验一致性（指标值
In the first stage, the weights of the sub-criteria are calculated using the fuzzy group BWM to determine the vendor scores for each criterion. The specific steps are as follows: secondary criteria for evaluating suppliers have been identified from economic, circular, social, and Industry 4.0 aspects by consulting literature and utilizing expert knowledge; each expert selects the optimal and worst sub-criteria respectively and conducts pairwise comparisons; the weights of the sub-criteria are calculated through an optimization model (CPLEX/GAMS) and consistency is verified (if the indicator value
2.使用模糊推理系统FIS(Using fuzzy inference system FIS)
在第二阶段，使用模糊推理系统FIS，计算供应商的最终综合分数。具体步骤如下：定义输入变量（经济、循环、社会、工业4.0）和输出变量（最终得分）的隶属函数；专家将隶属函数确定的输入和输出变量连接起来，通过专家知识建立625条模糊规则；在MATLAB中运行FIS，得到各供应商最终得分；根据得分对供应商进行排序。
In the second phase, using the fuzzy Inference System (FIS), calculate the final comprehensive score of the suppliers. The specific steps are as follows: define the membership functions for input variables (economic, circular, social, Industry 4.0) and output variables (final score); experts connect the input and output variables determined by the membership functions, establishing 625 fuzzy rules based on expert knowledge; run the FIS in MATLAB to obtain the final scores for each supplier; rank the suppliers according to their scores.
（2）结果分析（Result Analysis）
最终得分为0.672的供应商2和最终得分为0.598的供应商3分别被评为最佳和最差供应商。根据结果，可以得出两个结论，第一，供应商的最终得分并不是其准则得分的线性组合，第二，工业4.0准则得分对供应商的最终得分有显著影响，说明企业必须与重视数字化和智能化的供应商合作。
Supplier 2, with a final score of 0.672, and Supplier 3, with a final score of 0.598, were rated as the best and worst suppliers, respectively. Based on the results, two conclusions can be drawn: first, a supplier's final score is not a linear combination of its criterion scores, and second, the Industry 4.0 criterion scores have a significant impact on the suppliers' final scores, indicating that companies must collaborate with suppliers who prioritize digitalization and intelligence.
三、知识补充（Supplementary Knowledge）
GAMS又称为通用代数建模系统，是一种高层次的建模语言与优化求解环境，主要用于构建和求解数理规划问题。GAMS专注于优化建模，适合线性规划（LP）、混合整数规划（MIP）、非线性规划（NLP）、动态规划等问题；具有高度抽象的建模语言，使用接近数学表达式的语法描述变量、约束和目标函数；与多个优化求解器集成，可调用 CPLEX、GUROBI、CONOPT、MINOS 等主流优化器；跨行业应用于能源系统优化、供应链管理、交通运输、金融投资组合、环境规划等。
GAMS, also known as the General Algebraic Modeling System, is a high-level modeling language and optimization-solving environment primarily used to construct and solve mathematical programming problems. GAMS focuses on optimization modeling and is suitable for linear programming (LP), mixed-integer programming (MIP), nonlinear programming (NLP), dynamic programming, and other problems; it features a highly abstract modeling language that describes variables, constraints, and objective functions using syntax close to mathematical expressions; it integrates with multiple optimization solvers, allowing calls to mainstream optimizers such as CPLEX, GUROBI, CONOPT, MINOS, etc.; it is applied across industries in energy system optimization, supply chain management, transportation, financial portfolio management, environmental planning, and more.
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翻译：Google翻译
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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