摘要：Today, the editor will introduce the conclusion of an integrated group fuzzy inference and best–worst method for supplier selectio
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“颜读（43）：精读期刊论文《An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains》结论”
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"Yan Du (43): Careful reading of the journal paper ‘An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains’ Conclusion"
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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》结论的介绍。
Today, the editor will introduce the conclusion 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）研究贡献（Research Contributions）
本文提出了一个用于公私合作项目（PPP）中供应商评估与优先排序的决策支持系统。方法由两个阶段构成：第一阶段利用改进的群体最佳-最差法，从经济、循环、社会和工业4.0四个视角评估六家潜在供应商。第二阶段通过模糊推理系统（FIS）映射四大准则与最终得分之间的非线性关系。FIS结构包含625条模糊规则，结合离岸风电项目数据与四位专家的知识进行了验证。
This paper proposes a decision support system for supplier evaluation and prioritization in public-private partnership (PPP) projects. The method consists of two phases. In the first phase, a modified group best-worst method is used to evaluate six potential suppliers from four perspectives: economic, circular, social, and Industry 4.0. In the second phase, a fuzzy inference system (FIS) is used to map the nonlinear relationship between four criteria and the final scores. The FIS structure, comprising 625 fuzzy rules, was validated using offshore wind power project data and the knowledge of four experts.
（2）主要发现（Key findings）
本研究中的海上风电场项目数据和敏感性分析表明，FIS输出对工业4.0标准的敏感性高于其他标准。这意味着数字化与智能化能力在供应商选择中越来越关键。当各准则的子准则存在交叉依赖时，本方法在计算权重上会存在一定局限性。此时可考虑引入DEMATEL或WINGS等方法来刻画子准则间的相互作用。
The offshore wind farm project data and sensitivity analysis used in this study indicate that FIS outputs are more sensitive to Industry 4.0 criteria than to other criteria. This suggests that digitalization and intelligent capabilities are becoming increasingly crucial in supplier selection. This approach has limitations in calculating weights when sub-criteria within each criterion have cross-dependencies. In such cases, methods such as DEMATEL or WINGS could be considered to characterize the interactions between sub-criteria.
（3）研究不足与未来方向（Research Deficiencies and Future Directions）
文章的局限性为当前研究未能充分解决子准则之间的交叉依赖。未来研究方向可以结合DEMATEL、WINGS等方法处理指标间依赖关系。另外研究可以拓展至工业5.0背景下的循环供应商选择问题。目前相关研究几乎空白，是一个值得关注的未来研究主题。
A limitation of this article is that the current research fails to fully address the cross-dependencies between sub-criteria. Future research could incorporate methods such as DEMATEL and WINGS to address inter-criteria dependencies. Furthermore, the research could be extended to the issue of circular supplier selection in the context of Industry 5.0. Currently, this area of research is virtually nonexistent, making it a topic worthy of future attention.
三、知识补充（Supplementary Knowledge）
WINGS是一种多准则决策分析方法，主要用于处理复杂系统中不同指标之间的相互作用与权重分配问题。它最初是为了解决传统权重分配方法（如AHP、BWM等）难以有效处理指标之间非线性依赖关系的局限而提出的。与AHP、BWM假设各指标独立不同，WINGS能捕捉子准则之间的交叉依赖（例如：质量与声誉、绿色研发与污染控制等会互相影响）。它不仅仅是线性加权，而是通过非线性建模反映指标之间的“放大效应”或“削弱效应”。权重调整具有动态性，指标权重不是固定的，而是会随着其他指标的重要性和表现水平而动态变化，更符合实际决策场景。
WINGS is a multi-criteria decision analysis method primarily designed to address the interactions and weight assignment between different indicators in complex systems. It was originally proposed to address the limitations of traditional weight assignment methods (such as AHP and BWM), which struggle to effectively handle nonlinear dependencies between indicators. Unlike AHP and BWM, which assume the independence of each indicator, WINGS captures cross-dependencies between sub-criteria (for example, the mutual influence between quality and reputation, green R&D and pollution control, etc.). Rather than simply applying linear weighting, WINGS utilizes nonlinear modeling to reflect the "amplification" or "weakening" effects between indicators. Weight adjustment is dynamic; indicator weights are not fixed but change dynamically with the importance and performance of other indicators, making it more relevant to real-world decision-making scenarios.
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翻译：Google翻译
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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