摘要：This issue of tweets will introduce the 5.1 BWM results of the intensive reading journal paper "A fuzzy group decision-making mode
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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5.1BWM结果"。
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Today, the editor brings the "the 5.1 BWM 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5.1BWM结果。
This issue of tweets will introduce the 5.1 BWM 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）
为计算各方面和标准的重要程度，采取了以下步骤。首先，选定7位专家组成调查组，编制问卷发放给专家，要求他们依据会议解释填写表格、对指标发表意见。其次，使用BWM确定权重时，让专家分别表达衡量最佳标准相对其他标准、其他标准相对最差标准的重要性程度。再次，发放问卷前开会确定指标的最佳和最差属性及相应标准，如最佳为宏观风险，最差为内部风险。表6展示专家组收集的主要标准数据，包括最佳标准、最差标准等信息及每位专家的标准权重。最后，计算各准则的平均局部权重，研究显示宏观层面风险重要性最高，其次是操作层面风险和内部风险。
To calculate the importance of the aspects and criteria, the following steps were taken. First of all, 7 experts were selected to form a survey team, and questionnaires were prepared and sent to the experts, who were asked to fill in the form and express their opinions on the indicators according to the explanation of the meeting. Second, when using BWM to determine weights, ask the experts to express how important the best criteria are relative to the others, and the others relative to the worst criteria. Thirdly, a meeting was held before the questionnaire was issued to determine the best and worst attributes of the indicators and the corresponding standards, such as the best is macro risk and the worst is internal risk. Table 6 shows the key criteria data collected by the expert group, including information on the best criteria, the worst criteria, and the criteria weights for each expert. Finally, the average local weight of each criterion is calculated, and the study shows that the macro-level risk is the most important, followed by the operational level risk and internal risk.
然后，围绕风险子因素权重计算展开。针对宏观级别风险子因素权重，计算主要级别标准权重后，从专家组收集数据确定宏观级别风险子因素重要性。以C11为最佳标准、C13为最差标准，通过子因子平均局部权重乘以宏观水平组权重得出全局权重，C11、C12、C13和C14的全局权重分别为0.404、0.100、0.045和0.160。
Then, the calculation of risk sub-factor weight is carried out. For macro-level risk sub-factor weights, after calculating major level standard weights, data was collected from the expert group to determine the importance of macro-level risk sub-factors. Taking C11 as the best criterion and C13 as the worst criterion, the global weights are obtained by multiplying the average local weights of subfactors by the weights of macroscopic horizontal groups. The global weights of C11, C12, C13 and C14 are 0.404, 0.100, 0.045 and 0.160, respectively.
针对操作风险子因素权重，同样方法计算操作风险全局权重，以C23为最佳标准、C21为最差标准，根据7位专家问卷和访谈计算局部权重，再结合操作风险类别相应权重得出全局权重。所有操作风险重要性排序为C23>C24>C22>C25>C21。
For the weight of operational risk sub-factors, the global weight of operational risk was calculated by the same method, with C23 as the best standard and C21 as the worst standard. The local weight was calculated according to the questionnaire and interview of 7 experts, and the global weight was obtained by combining the corresponding weight of operational risk categories. The importance of all operational risks is ranked as C23 > C24 > C22 > C25 > C21.
关于专家意见的内部风险类别中的子因素的信息如表9所示。根据专家决策，C33为最佳标准，C31为最差标准。基于每个专家的不同B-t-O和O-t-W向量，计算每个准则的局部权重。使用表6中内部风险类别的局部权重和标准的平均局部权重，计算内部风险类别中风险因素的全局权重。由结果可知，C33和C31在内部危险因素中的权重系数最高和最低。
Information on the sub-factors in the internal risk category of expert opinion is shown in Table 9. According to expert decision, C33 is the best standard and C31 is the worst standard. The local weights for each criterion are calculated based on the different B-t-O and O-t-W vectors for each expert. Use the local weights for the internal risk categories in Table 6 and the standard average local weights to calculate the global weights for the risk factors in the internal risk categories. The results show that C33 and C31 have the highest and lowest weight coefficients in internal risk factors.
四、知识补充——BWM方法中的B-t-O和O-t-W向量（Knowledge Supplement - B-t-O and O-t-W vectors in the BWM method）
BWM方法中的 B-t-O（Best-to-Others）向量 和 O-t-W（Others-to-Worst）向量 是其权重计算的核心步骤，用于简化多准则决策中的两两比较过程。以下是具体说明：
B-t-O (Best-to-Others) and O-t-W (others-worst) vectors in BWM are the core steps of weight calculation, which are used to simplify pairwise comparison process in multi-criteria decision making. Here are the details:
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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