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今天小編為您帶來精讀複刻論文《基于CWPHM算子和C-DEMATEL的語言型多屬性決策方法》之代碼複刻（19）。
歡迎您的通路！
Share interest, spread happiness,
increase knowledge, and leave beautiful.
Dear, this is the LearingYard Academy!
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)".
Welcome to visit!
一、内容摘要（Content Summary）
本期推文将從思維導圖、精讀内容、知識補充三個方面介紹精讀複刻論文《基于CWPHM算子和C-DEMATEL的語言型多屬性決策方法》的代碼複刻（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、思維導圖（Mind Mapping）
三、精讀内容（Detailed Reading Content）
上期推文運用CWAA算子對各幹部資訊進行了集結，得到了五位備選幹部的綜合資訊值（綜合雲）。本期将運用綜合雲，進行雲正向發生器，來模拟雲滴，計算進行了集結，得到了五位備選幹部的總計分，進而進行方案排序，即，利用參考文獻[35]定義3中的雲模型總計分方法，計算各方案的總計分。若總計分的值越大，則方案Oi的排序越靠前，反之越靠後。該總計分的方法與本文中采用的方法一緻，如下圖所示。
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.
其代碼實作和運作結果如下圖所示。
The implementation code and its execution results are illustrated in the following figure.
根據代碼實作的結果來看，備選幹部1與2的排序有所差别，可能是前期文本中資料計算錯誤的原因。
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、知識補充——CWAA算子（Supplementary Knowledge — CWAA Operator）
CWAA算子是一種多屬性決策方法，它通過将屬性的權重和分數進行組合，得出最終的綜合得分。與傳統的權重平均方法不同，CWAA算子允許在屬性權重上引入不确定性，以更好地處理模糊性資訊。CWAA算子可以靈活地适應不同屬性之間的權衡和不确定性，使其在多領域應用中具有廣泛的價值。
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.
CWAA算子方法廣泛應用于投資決策、風險管理、供應鍊優化等領域。它有助于決策者更好地應對多屬性決策問題，特别是在面對不确定性和模糊性資訊時。
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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參考資料：ChatGPT、百度百科
參考文獻：
王偉明, 徐海燕, 朱建軍等. 基于CWPHM算子和C-DEMATEL的語言型多屬性決策方法 [J/OL]. 中國管理科學, 2023, 1(1): 1-13.
本文由LearningYard學苑整理并發出，如有侵權請在背景留言！
文案| Ann
排版| Ann
稽核| Wei