臺灣博碩士論文加值系統

現實生活中，多準則決策（Multiple Criteria Decision Making, MCDM）的應用非常廣泛，人們經常面臨涉及多個目標或準則相互衝突的決策問題。文獻已提供各種MCDM方法協助決策者提高決策品質。多準則決策方法透過不同的運算方式，加入權重以表示決策者的偏好，對方案內的準則進行評估，最後得到一組排序，供決策者參考。但這組排序是否即為符合決策者的偏好，其實難以確認，因為人們處理訊息的能力是有限的、難以釐清眾多方案數據間的關係。因此決策者可能無法得知多準則方法的方案排序，是否即為心中的理想方案。且文獻中大部分的MCDM方法皆未提出相關的信效度分析以證實所提出決策工具建議的排序即為決策者的理想排序。此外，在啟發決策者偏好時，決策者可能受認知偏誤理論影響，提供不一致的偏好資訊。例如觀點理論即主張基於參考點的不同將影響決策者對風險的態度。因此在不確定條件下的決策選擇，往往取決於結果與預期間的差距，而非結果本身。
綜合以上，本研究目的係檢驗並比較既有七種多準則決策方法的有效性，並建立一套模擬數據實驗流程，比較MCDM方法建議的方案排序與偏好效用函數所模擬決策者之排序的差異。同時，本研究並測試對MCDM方法有效性的控制變因，包括生成不同準則數、分配方式、是否為非凌越解集合的方案集合及三種權重方法等。在決策者偏好係用函數的部分，除了補償性、非補償性及部分補償的效用函數外，亦加入觀點理論之效用函數。針對方案間的比較，給予偏好效用函數不同的參數設定。決策者對風險的態度可分為風險中立、風險趨避及風險愛好三種，以表達觀點理論的主張。
本研究結果顯示互動式方法能更準確提供符合決策者偏好之方案，而互動式方法中的分段線性觀點理論法（piecewise linear prospect theory method, PLP）所需的互動次數遠少於分析層級程序法（analytic hierarchy process, AHP）。此外，ELECTRE TRI法受限於門檻值之設定，較難以提供符合決策者的理想排序，因此不適合排序方案。目前的MCDM方法仍多以線性為基礎，使用者在選擇決策工具時應留心，且未來發展相關決策方法時可將不同的決策者偏好效用函數納入考量。

Multi-criteria decision making has various applications in real life. We face Multiple Criteria Decision-Making (MCDM) problems every day and their criteria very often conflict with each other. MCDM methods have been developed to support decision makers to enhance decision quality. MCDM methods use various calculation methods to evaluate the rank of alternatives. Yet, little evidence could support that the best alternative chosen by MCDM method is same with the decision maker’s ideal alternative. On the other hand, decision makers may provide inconsistent preferences due to cognitive biases. For example, the prospect theory advocates that decision makers show different risk attitudes according to different reference points.
Therefore, this study aims to examine and compare existing seven seven MCDM methods including TOPSIS, VIKOR, ELECTRE and the piecewise linear prospect theory method (PLP) in terms of effectiveness by using simulation experiements. Controlling variables include number of alternatives, number of criteria, distribution of data set and dominated or non-dominated data set. We also add three different weight combination in these experiments to see how weights affect the MCDM methods. We test four different utility functions. Except the compensatory, non-compensatory and portion of compensatory utility functions, we also use the prospect theory utility function. After that, we compare the MCDM methods’ ranks with decision maker’s ranks by using assumed preference utility functions. The result shows that interactive methods such as PLP and AHP can provide accurate rank to reflect decision makers’ true wishes. Added to this, PLP can use less interactive times than AHP. All of these methods are based on linear utility function, future research might add different utility concept in MCDM methods.

目錄 i
表目錄 iii
圖目錄 iv
第一章 緒論 1
1.1 研究背景、動機與重要性 1
1.2 研究目的與研究範圍 5
1.3 論文結構 6
第二章 文獻回顧 7
2.1 多準則決策 7
2.2 多準則決策工具 8
2.3 決策陷阱 13
2.4 多準則決策輔助工具 17
第三章 研究方法 20
3.1 研究設計 20
3.1.1 實驗數據 22
3.1.2 實驗方法 26
3.2 研究架構 37
第四章 實驗結果與分析 39
第五章 結論 46
5.1. 研究結論 46
5.2. 後續研究建議與研究限制 46
參考文獻 48
附錄A：觀點理論－確定效應實驗 54
附錄B：觀點理論－反射效應實驗 55
附錄C：觀點理論－分離效應實驗 56
附錄D：情境效應（Context effect） 56
附錄E：可視化(visualization) 64
附錄F：逆轉排序（Rank reversal） 65
附錄G：R程式碼-生成實驗數據 67
附錄H：R程式碼-實驗方法排序 70
附錄I：R程式碼-決策者效用函數排序 77
附錄J：R程式碼-小工具 79

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