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Today, we bring you a code replica of the intensive reading and reproduction paper "A Language-based Multi-attribute Decision Making Method Based on CWPHM Operators and C-DEMTEL" (19).
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Today, the editor brings the "intensive reading replica paper 'A linguistic Multi-Attribute Decision-Making approach based on the CWPHM operator and C-DEMATEL' for code reproduction (19)".
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一、内容摘要（Content Summary）
In this issue, we will introduce the code fork of the intensive reading and forking paper "Linguistic Multi-attribute Decision Making Method Based on CWPHM Operator and C-DEMATEL" from three aspects: mind map, intensive reading content, and knowledge supplementation (19).
In this issue, we will introduce the code replication (19) of the refined reading paper "Language Type Multi-Attribute Decision-Making Method Based on CWPHM Operator and C-DEMATEL" from three aspects: mind map, refined reading content, and supplementary knowledge.
二、思维导图（Mind Mapping）
三、精读内容（Detailed Reading Content）
In the last tweet, the CWAA operator was used to assemble the information of each cadre, and the comprehensive information value of the five candidate cadres was obtained (comprehensive cloud). In this issue, we will use the integrated cloud to carry out the cloud forward generator to simulate the cloud droplets, calculate the assembly, and obtain the total scores of the five candidate cadres, so as to sort the schemes, that is, the total score of each scheme is calculated by using the cloud model total score method in Reference [35] Definition 3. If the value of the total score is larger, the Oi of the scheme will be ranked higher, and vice versa. The method used to add the score is consistent with the one used in this article, as shown in the figure below.
In the previous article, the CWAA operator was employed to aggregate information on various cadres, resulting in the comprehensive information value (referred to as "cloud synthesis") of five alternative cadres. In this installment, utilizing the cloud synthesis, a cloud-based positive generator is employed to simulate cloud droplets. Through aggregation, the total scores of the five alternative cadres are calculated to facilitate scheme prioritization. Specifically, employing the method defined in reference [35], the cloud model total score method, the total scores of each scheme are computed. A higher total score indicates a higher ranking for scheme Oi, and vice versa. The total score method employed here aligns with the method utilized in this paper, as depicted in the following figure.
The code implementation and running results are shown in the following figure.
The implementation code and its execution results are illustrated in the following figure.
According to the results of the code implementation, the order of alternative cadres 1 and 2 is different, which may be the reason for the error in the calculation of the data in the previous text.
Based on the results of the code implementation, there are differences in the ranking of alternative cadres 1 and 2, which may be attributed to calculation errors in the preceding data.
四、知识补充——CWAA算子（Supplementary Knowledge — CWAA Operator）
The CWAA operator is a multi-attribute decision-making method, which combines the weights and scores of attributes to obtain the final comprehensive score. Different from the traditional weighted average method, the CWAA operator allows to introduce uncertainty into the attribute weights to better handle fuzzy information. The CWAA operator can flexibly adapt to the trade-offs and uncertainties between different attributes, making it of wide value in multi-domain applications.
The CWAA operator is a multi-attribute decision-making method that combines attribute weights and scores to derive the final comprehensive score. Unlike traditional weighted average methods, the CWAA operator allows for the introduction of uncertainty in attribute weights to better handle fuzzy information. It flexibly adapts to trade-offs and uncertainties among different attributes, rendering it widely valuable across various domains.
The CWAA operator method is widely used in investment decision-making, risk management, supply chain optimization and other fields. It helps decision-makers better cope with multi-attribute decision-making problems, especially when faced with uncertainty and ambiguous information.
The CWAA operator method finds extensive applications in investment decision-making, risk management, supply chain optimization, among other areas. It assists decision-makers in better addressing multi-attribute decision problems, particularly in the presence of uncertainty and fuzzy information.
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That's all for today's sharing.
If you have a unique idea about the article,
please leave us a message,
and let us meet tomorrow.
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参考资料：ChatGPT、百度百科
Bibliography:
Wang Weiming, Xu Haiyan, Zhu Jianjun et al. Language-based multi-attribute decision-making method based on CWPHM operator and C-DEMATEL [J/OL]. CMS, 2023, 1(1): 1-13.
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