安玲学记(177)——精读期刊论文1.引言

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Today, the editor brings the "the 1. introduction of the journal paper 'MARCOs-based two-dimensional language intuitive multi-attribute group decision Method'".

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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《基于MARCOS的二维语言直觉多属性群决策方法》的引言。

This issue of tweets will introduce the introduction of the intensive reading journal paper "MARCOs-based two-dimensional language intuitive multi-attribute group decision Method" 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）

在引言部分，作者首先介绍了多属性群决策的定义。多属性群体决策（MAGDM）问题是决策理论研究的重要组成部分，在经济、管理甚至日常社会领域得到了广泛的关注。

In the introduction, the author first introduces the definition of multi-attribute group decision making. Multi-attribute group decision making (MAGDM) is an important part of decision theory research, and has been widely concerned in the fields of economy, management and even daily society.

然后，对决策问题中的模糊环境及其方法、语言变量信息做了相关的文献综述。

Then, the paper makes a literature review on fuzzy environment, its methods and language variable information in decision problems.

为了处理复杂不确定性的问题，经典模糊集被拓展为了直觉模糊集。直觉模糊集同时包含隶属度和非隶属度两个刻画标度，能描述专家对某一方案在某特定属性下的支持度、反对度以及犹豫度，克服了经典模糊集的不足。

In order to deal with the problem of complex uncertainty, the classical fuzzy set is extended to the intuitionistic fuzzy set. Intuitionistic fuzzy set contains two scales of membership degree and non-membership degree at the same time, which can describe the expert's support degree, opposition degree and hesitation degree of a scheme under a certain attribute, overcoming the shortcomings of classical fuzzy set.

当实际问题过于复杂或不明确时，由于自身知识能力与主观经验的局限性，使得决策者很难用给出精确的评价信息。与精确的评价信息相比，语言变量更接近人类表达知识的方式，能更好地反映人们认知的模糊性和不确定性信息。近年来，学者们在语言变量及其它模糊集的方面做了较多的工作，提出了多种语言模糊集，如语言直觉模糊集、语言犹豫模糊集、单值中智语言模糊集以及Pythagorean模糊语言集等。

When the actual problem is too complicated or unclear, it is difficult for decision makers to give accurate evaluation information due to the limitations of their own knowledge ability and subjective experience. Compared with accurate evaluation information, linguistic variables are closer to the way of human knowledge expression and can better reflect the fuzzy and uncertain information of people's cognition. In recent years, scholars have done a lot of work on language variables and other fuzzy sets, and proposed a variety of language fuzzy sets, such as language intuitive fuzzy sets, language hesitation fuzzy sets, single value neutral language fuzzy sets and Pythagorean fuzzy sets.

接着，作者介绍了MARCOS方法的定义及其特点。MARCOS方法基于正理想方案和负理想方案之间的关系确定方案的效用函数，然后对正理想解和负理想解进行一致性排序，是一种简单有效的多准则方法。与其它方法相比，MARCOS方法具有简单、有效、易于排序和优化流程的特点。和 TOPSIS方法相比，MARCOS方法不需要计算距离，因此可以避免欧式距离计算过程中指标之间的干扰，而造成计算结果出现偏差的可能性。

Then, the author introduces the definition and characteristics of MARCOS method. MARCOS method is a simple and effective multi-criterion method to determine the utility function of the scheme based on the relationship between positive and negative ideal solutions, and then order the positive and negative ideal solutions in a consistent manner. Compared with other methods, MARCOS method is simple, effective and easy to sort and optimize the process. Compared with TOPSIS method, MARCOS method does not need to calculate the distance, so it can avoid the interference between the indicators in the process of Euclidean distance calculation, which may cause the possibility of calculation results.

最后，作者介绍了MARCOS方法的研究空白以及本文的研究内容。目前还没有相关的研究将MARCOS方法引入到直觉模糊集、语言直觉模糊集、二维语言变量或者犹豫模糊集等环境中。基于此，本课题将直觉模糊集和二维语言变量 融合提出二维语言直觉的概念，并将MARCOS方法拓展到二维语言直觉环境以处理模糊决策问题中信息的不确定性和模糊性。

Finally, the author introduces the research blank of MARCOS method and the research content of this paper. At present, there is no relevant research to introduce MARCOS method to intuitionistic fuzzy sets, linguistic intuitionistic fuzzy sets, two-dimensional linguistic variables or hesitant fuzzy sets. Based on this, the concept of two-dimensional language intuition is proposed by integrating intuitionistic fuzzy sets with two-dimensional language variables, and the MARCOS method is extended to two-dimensional language intuition environment to deal with the uncertainty and fuzziness of information in fuzzy decision problems.

四、知识补充——MARCOS方法的应用（Knowledge supplement - Application of MARCOS method）

MARCOS方法的应用范围广泛，不仅限于特定的领域或问题，而是可以应用于各种需要多属性决策的场景。例如，在配电网设备风险评估中，MARCOS方法被用来计算各待评估设备的效用函数，并根据这些函数值对设备的风险程度进行排序，从而评估设备的风险?。此外，在负荷侧资源响应潜力评估中，MARCOS方法也被用来计算各类负荷的响应潜力效用函数值，得到各类负荷的响应潜力排序结果?。这些应用展示了MARCOS方法在复杂决策问题中的有效性和适用性。

The application of MARCOS method is not limited to a specific field or problem, but can be applied to a variety of scenarios requiring multi-attribute decision making. For example, in the risk assessment of distribution network equipment, MARCOS method is used to calculate the utility functions of each equipment to be evaluated, and rank the risk degree of the equipment according to these function values, thus evaluating the risk of the equipment ?. In addition, in the evaluation of resource response potential at load side, MARCOS method is also used to calculate the utility function values of response potential of various loads, and the ranking results of response potential of various loads are obtained ?. These applications demonstrate the effectiveness and applicability of MARCOS in complex decision problems.

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参考资料：ChatGPT、百度百科

参考文献：

许雷, 刘熠, 刘芳等. 基于MARCOS的二维语言直觉多属性群决策方法 [J]. 模糊系统与数学, 2022, 36(5): 128-141.

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文案| Ann

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