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題名：結合隨機森林與多準則決策模型評估綠色供應商管理
作者：鄭嘉華
作者(外文)：Chia-Hua Cheng
校院名稱：國立臺北科技大學
系所名稱：管理學院管理博士班
指導教授：邱垂昱
劉建浩
學位類別：博士
出版日期：2018
主題關鍵詞：敏感度分析；多準則決策；綠色供應商管理；複合比例評估結合灰關聯；決策實驗室分析法；一致的模糊偏好關係；網路層級分析法；隨機決策森林；資料探勘；Sensitivity analysis；MCDM；GSM；COPRAS-G；DEMATEL；CFPR；ANP；Random Forest；Data mining
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在今天全球化與工業化影響之下，已經造成環境污染日益增加。由於政府的立法和人們對保護環境的意識的提高，使得企業必須更加重視此一問題。其所衍生的綠色供應鏈管理(Green supplier chain management, GSCM)相關問題也越來越受到重視。因此，如何將環境考慮整合到原本的供應鏈中，是企業目前亟待解決的問題。綠色環保業績已成為供應商管理的必要指標，供應商管理層應該有適當的評估方法，定期評估與改善供應商綠色績效。因此，本研究目的在建立一個綠色供應商績效改善的多準則決策(Multiple criteria decision making, MCDM)評估模式。不同於以往多準則決策模式，大多採用專家問卷或文獻探討方式建立評估模式，本研究採用隨機森林數據挖掘方法，從過去稽核資料中提取必要的指標。然後，使用決策試驗分析法(Decision making trial and evaluation laboratory, DEMATEL)，瞭解指標或準則之間因果關係的結構，DEMATEL所獲得因果關係可以建構網絡程序分析(Analytic network process, ANP)之評估架構。為了提高傳統ANP不容易達到一致性的結果，以及減少成對比較的次數，本研究使用一致的模糊偏好關係的網絡程序分析(Consistent fuzzy preference relations based analytic network process, CFPR-ANP)方法，求取各準則的權重值。最後，考量資訊的不確定性，本研究使用組合複合比例評估結合灰關聯(Complex proportional assessment of alternatives with grey relations, COPRAS-G)方法進行供應商的績效評估，以獲得每個供應商與渴望水準的績效差距，並提供在不確定的情況下為公司選擇最佳供應商的方法。本研究將以國內某電子廠所提供的資料進行實證分析，說明本模式的有效與實用性。
Under the influence of globalization and industrialization, environmental pollution is getting worse and impacts our life every day. Public awareness of environmental protection and government legislation have forced enterprises to be more concerned about this issue. As a result, enterprises are paying more attention to green supply chain management (GSCM) related problems. Therefore, how to integrate environmental considerations into a firm’s existing supply chain is an urgent issue that needs to be solved by enterprises. Green environmental performance has become a necessary indicator for supplier management, and enterprises should have in place appropriate evaluation methods to regularly evaluate and improve the suppliers green performance. Therefore, the purpose of this study is to establish a multiple criteria decision making (MCDM) model to improve green supplier performance. Unlike previous multi-criteria models that are reliant upon expert questionnaires or literature surveys to form the evaluation criteria, this study applies the random forest data mining method, to extract the essential indicators from past auditing datasets. Then, the decision making trial and evaluation laboratory (DEMATEL) is adopted to understand the structure of cause-effect relationships between the indicators. The obtained cause-effect relationships are used for analytic network process (ANP) analysis. In order to improve the consistency and reduce the number of pairwise comparisons needed for ANP, a consistent fuzzy preference relations based analytic network process (CFPR-ANP) method is used to derive the weights of the criteria. Finally, by considering the uncertainty of information, the combined complex proportional assessment of alternatives with grey relations (COPRAS-G) is used to evaluate supplier performance. The COPRAS-G can explore the gap to the aspiration level for each supplier, providing a method to choose the best supplier for the company under uncertainty. This study uses an electronics company as the empirical example to demonstrate the effectiveness and practicality of the proposed model.
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