摘要：This issue of tweets will introduce the 5.2. fuzzy MARCOS results of the intensive reading journal paper "A fuzzy group decision-m
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今天小编为大家带来“精读期刊论文《衡量食品供应链弹性的模糊群体决策模型：西班牙案例研究》的5.2.模糊MARCOS结果"。
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Today, the editor brings the "the 5.2. fuzzy MARCOS results of the journal paper 'A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain'".
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一、内容摘要（Content summary）
本期推文将从思维导图、精读内容、知识补充三个方面介绍精读期刊论文《衡量食品供应链弹性的模糊群体决策模型：西班牙案例研究》的5.2.模糊MARCOS结果。
This issue of tweets will introduce the 5.2. fuzzy MARCOS results of the intensive reading journal paper "A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain" from three aspects: mind mapping, intensive reading content, and knowledge supplement.
二、思维导图（Mind Mapping）
三、精读内容（Detailed Reading Content）
作者描述了模糊MARCOS方法在评估食品供应链（FSC）中关键参与者弹性措施的应用过程。首先，通过7位专家基于模糊量表对关键参与者的风险因素进行评分构建初始决策矩阵，并结合专家评价（表11）进行归一化处理生成综合模糊决策矩阵。接着，利用相关方程确定模糊理想解与反理想解，构建加权归一化聚合决策矩阵（表12），其中权重向量由最佳最差法（BWM）获得。随后，基于加权决策矩阵计算模糊加权矩阵参数之和及模糊效用度，通过理想解与反理想解结合特定公式计算模糊效用函数，最终得出每个备选方案的效用函数并展示于表14。最后，采用去模糊化方法计算折衷分数以完成相关排名，结果汇总于表15，为西班牙案例研究提供了系统化的评估依据。
The authors describe the application of the fuzzy MARCOS method to assess the resilience measures of key players in the food supply chain (FSC). First, seven experts scored the risk factors of key participants based on the fuzzy scale to construct the initial decision matrix, and combined with the expert evaluation (Table 11) for normalization processing to generate a comprehensive fuzzy decision matrix. Then, the correlation equations are used to determine the fuzzy ideal and anti-ideal solutions, and the weighted normalized aggregate decision matrix is constructed (Table 12), where the weight vector is obtained by the best worst method (BWM). Then, the sum of fuzzy weighted matrix parameters and fuzzy utility are calculated based on the weighted decision matrix, and the fuzzy utility function is calculated by combining the ideal solution and the anti-ideal solution with specific formulas. Finally, the utility function of each alternative scheme is obtained and shown in Table 14. Finally, the compromise score was calculated using the de-fuzzification method to complete the relevant ranking, and the results are summarized in Table 15, providing a systematic evaluation basis for the Spanish case study.
然后作者展示了MARCOS参数优化器的去模糊化值，并用于分析基于风险标准的排名结果（值越低表示选择越好）。结果显示，A1（农民）是弹性最小的替代选项，表明农民对环境条件最为敏感；而A6（超市）则在风险环境中表现出最佳弹性，被认定为最具适应性的关键参与者。此外，A5（分销商）位列第二大弹性关键参与者，A3（另一分销商）紧随其后排名第三，两者妥协得分差异较小。根据该评估，小型零售商排名第四，加工商和物流平台分别排在第五和第六位，整体反映了食品供应链中各关键参与者的弹性水平及其在风险环境下的表现排序。
The authors then present the defuzzification values of the MARCOS parameter optimizer and use them to analyze the ranking results based on risk criteria (lower values indicate better choices). The results show that A1 (farmer) is the least elastic alternative, indicating that farmers are the most sensitive to environmental conditions. A6 (supermarkets), on the other hand, showed the best resilience in the risk environment and was identified as the most adaptable key player. In addition, A5 (distributor) ranked as the second most resilient key player, followed by A3 (another distributor) in third place, with a smaller difference in compromise scores. According to the assessment, small retailers ranked fourth, processors and logistics platforms fifth and sixth, respectively, reflecting the overall level of resilience of the key players in the food supply chain and their performance ranking in a risk environment.
四、知识补充——去模糊化（Knowledge Supplement - Deblurring）
去模糊化是将推论所得到的模糊值转换为明确的控制讯号，做为系统的输入值。去模糊化是模糊推理机中重要的一步，也称解模糊化。去模糊化的方法有很多种，最常用的有最大隶属度法，重心法和加权平均法。
Defuzzification is to convert the fuzzy value obtained by inference into an explicit control signal, which is used as the input value of the system. Defuzzification is an important step in fuzzy inference machine, also known as defuzzification. There are many methods of defuzzification, the most commonly used are the maximum membership degree method, the center of gravity method and weighted average method.
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参考资料：ChatGPT、百度百科
参考文献：
Yazdani Morteza, Torkayesh Ali Ebadi, Chatterjee Prasenjit, et al. A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain [J]. Socio-Economic Planning Sciences, 2022, 82(1): 101257-101271.
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来源：LearningYard学苑