臺灣博碩士論文加值系統

多屬性效用理論(multi-attribute utility theory, MAUT)是利用個人表達出主觀偏好，藉著數學方法來建構效用函數，考慮屬性之組合來進行方案評估。多屬性效用理論常見的整合方式是透過簡單加權來求得最終的效用值，並沒有將決策群體中個人偏好差異程度與順序偏好考量在模式之中，為了達到群體整合之目的，僅利用簡單之數學加權方法來整合決策者的意見，顯得過於單純，無法把決策者對於方案之間偏好程度以及排序充分表現在最後的整合結果上。因此，本研究以多屬性效用理論於群體決策偏好整合之問題，提出以「群體偏好整合」為基礎的模式。
整合模型階段分為兩部份，第一部份針對決策者對於偏好之在意程度，利用「方案之間效用第二動差」與「方案之間效用第三動差」作為群體決策偏好整合之係數，方案之效用拉開差距之後，可充分表現出決策者於不同方案間分散與對稱程度；第二部份針對偏好順序之整合模式，利用「方案偏好順序之係數」考慮於模型中，因為決策者會在意最偏好的方案是否被採用，以上兩個部份都是為了將現實生活中決策者之決策心理考量在模式中，故將兩階段的係數加以結合，最後得到效用鑑別值(utility discriminative value)。透過實證演算得到之結果，將本研究-「群體偏好之整合模型」與簡單加權模式進行比較，了解兩者整合模型之差異，並透過滿意度指標以及回饋性訪談予以衡量，顯示本研究-「群體偏好之整合模型」較傳統簡單加權之方式所得到之滿意度略高。

Multi-attribute utility theory (MAUT) utilizes individual’s preference and constructs utility function by mathematics method to consider the combination of attributes in assessing alternatives. A common aggregation method of MAUT for group decisions is the simple additive weighting (SAW) method which does not consider individual’s preference levels and preference priorities for decision makers. In achieving the purpose for group decision aggregation, it seems too intuitive for SAW method. In this paper, preference differences which denote the preference degrees among different alternatives for each decision maker, and preference priorities which denote the rank lists for the alternatives for each decision maker, are both considered to construct the utility discriminative values of group members for group decision problems. Finally, a comparative analysis is performed to compare the proposed model to the SAW model, and a satisfaction index is proposed to indicate the satisfactory level of such a final group decision. An “Expert feedback interview” is conducted to investigate the differences between the proposed approach and the SAW model which aggregate the decision by the simple additive weighting method.

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 3
第四節 研究流程 4
第五節 論文架構 4
第二章 文獻探討 6
第一節 多準則決策方法(Multiple Criteria Decision Making) 6
第二節 多屬性效用理論 9
一、 多屬性效用理論之概念與應用 9
二、 多屬性效用理論與不確定性 12
三、 多屬性效用理論之批判 13
第三節 群體決策 15
一、 群體決策之方法 15
二、 多屬性效用理論之偏好整合 17
第三章 群體偏好之整合模型 20
第一節 問題描述 20
第二節 研究架構 27
第三節 模式構建 28
一、 偏好程度整合階段之「各方案之間效用第二動差」 30
二、 偏好程度整合階段之「各方案之間效用第三動差」 31
三、 偏好順序整合階段之「方案偏好順序之係數」 34
第四節 衡量偏好整合模型之滿意指標 37
一、 差異係數 37
二、 方案偏好順序差異 39
三、 滿意指標 39
第四章 實證研究 41
第一節 新產品發展 41
一、範例說明 41
二、本研究模式-「群體偏好之整合模型」 42
三、討論 44
第二節 旅遊地點之選擇 45
一、研究對象與決策過程 45
二、本研究模式-「群體偏好之整合模型」 48
三、討論 50
第五章 結論 53
第一節 研究成果 53
第二節 研究限制 54
第三節 未來研究方向 55
參考文獻 56
附 錄 62

圖目錄
圖1.1 研究流程圖 5
圖3.1 研究架構圖 28
圖3.2 研究步驟圖 29

表目錄
表4.1 決策者對於各方案之效用 42
表4.2 決策者對於各方案效用鑑別值 43
表4.3 旅遊地點之樣本統計表 46
表4.4 決策者對於各方案之效用 47
表4.5 決策者對於各方案之效用鑑別值 49

中文部分【依筆劃順序】
廖經泰(民94)，層級分析法於群體決策偏好整合之研究，國立成功大學工業管理研究所碩士論文。

鄧振源，計畫評估-方法與應用，海洋大學運籌規劃與管理研究中心，2002。
英文部分
Allais, M. (1952), “The foundations of a Positive theory of choice involving risk and a criticism of the postulates and axioms of the American school,” Expected utility and the Allais paradox, pp.27-145.

Ananda, J. and Herath, G. (2005), “Evaluating public risk preference in forest land-use choices using multi-attribute utility theory,” Ecological Economics, 55, pp.408-419.

André, F.J. and Riesgo, L. (2007), “A non-interactive elicitation method for non-linear multiattribute utility functions: Theory and application to agricultural economics,” European Journal of Operational Research, 181, pp.793-807.

Baucells, M. and Sarin, R.K. (2003), “Group decisions with multiple criteria,” Management Science, 49, pp.1105-1118.

Benayoun, R., Roy, B. and Sussman, N. (1966), “Manual de reference du programme ELECTRE: note de synthese et formation,” Direction Seientifique SEMA, 25.

Bernoulli, D. (1954, first published 1738), “Exposition of a new theory of the measurement of risk,” Econometrica, 22, pp.23-36.

Bodily, S.E. (1979), “A delegation process for combining individual utility functions,” Management Science, 25, pp.1035-1041.

Chan, C.L. and Chang, C.C. (1998), “A method combining MAU and Fuzzy logic for cooperative decision making,” Computers ind. Engng, 35, pp.291-294.


Chang, Y.H. and Yen, C.H. (2001), “Evaluating airline competitiveness using multiattribute decision making,” Omega, 29, pp.405-415.

De Almeida, A.T., “Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method,” Computers & Operations Research, (Article in Press, 2006).

Deng, H., Yeh, C.H. and Willis, R.J. (2000), “Inter-company comparison using modified TOPSIS with objective weights,” Computers & Operations Research, 27, pp.963-973.

Dowling, K.L. and St. Louis, R.D. (2000), “Asynchronous implementation of the nominal group technique: is it effective?,” Decision Support Systems, 29, pp.229–248.

Dyer, J.S., Fishburn, P.C., Steuer, R.E., Wallenius, J. and Zionts, S. (1992), “Multiple criteria decision making, multiattribute utility theory: the next ten years,” Management Science, 38, pp.645-654.

Edwards, W. (1977), “How to use multi-attribute utility measurement for social decision making,” IEEE Transaction on Systems, Man, and Cybernetics, SMC7(5), pp.326-340.

Fishburn, P.C. (1970), Utility theory for decision making, John Wiley & Sons, New York.

Frisch, R.(1895-1973) and Bjerkholt, O.(1995), Foundations of modern econometrics: the selected essays of Ragnar Frisch, Edward Elgar, Brookfield, Vt., US.

Hwang, C.L., Lai, Y.J. and Liu, T.Y. (1993), “A new approach for multiple objective decision making,” Computers & Operations Research, 20, pp.889-899.

Hwang, C.L. and Lin, M.J. (1987), Group Decision Making Under Multiple Criteria, Springer-Verlag, Berlin.

Hwang, C.L. and Yoon, K. (1981), Multiple Attribute Decision Making Methods and Applications: A State-of-the-Art Survey, Springer-Verlag, New York.

Jensen, N.E. (1967), “An introduction to Bernoulli utility theory. I. Utility functions,” Swedish Journal of Economics, 69, pp.163-183.

Kahneman, D. and Tversky, A. (1979), “Prospect theory: an analysis of decision under risk,” Econometrica, 47, pp.263-291.

Keeney, R.L. and Raiffa, H. (1976), Decision with multiple objectives: preference and value trade offs, John Wiley & Sons, New York.

Kim, J.K. and Choi, S.H. (2001), “A utility range-based interactive group support system for multiattribute decision making,” Computers & Operations Research, 28, pp.485-503.

Kim, S.H. and Han, C.H. (1999), “An interactive procedure for multi-attribute group decision making with incomplete information,” Computers & Operations Research, 26, pp.755-772.

Kirkwood, C.W. and Corner, J.L. (1993), “The effectiveness of partial information about attribute weights for ranking alternatives in multiattribute decision making,” Organizational Behavior and Human Decision Process, 54, pp.456-476.

Lai, S.K. (1995), “A preference-based interpretation of AHP,” Omega, 23, pp.453-462.

Le Galès, C., Buron, C., Costet, N. and Rosman, S., (2002). “Development of a preference-weighted health status classification system in France: The Health Utilities Index 3,” Health Care Management Science, 5, pp.41–51.

Lichtenstein, S. and Slovic, P. (1971), “Reversals of preferences between bids and choices in gambling decisions,” Journal of Experimental Psychology, 89, pp.46-55.

Linares, P. and Romero, C. (2002), “Aggregation of preference in an environment economics context: a goal-programming approach,” Omega, 30, pp.89-95.



Mateos, A., Jiménez, A. and Ríos-Insua, S. (2006), “Monte Carlo simulation techniques for group decision making with incomplete information,” European Journal of Operational Research, 174, pp.1842-1864.

Matsatsinis, N.F., Grigoroudis, E. and Samaras, A. (2005), “Aggregation and disaggregation of preferences for collective decision-making,” Group Decision and Negotiation, 14, pp.217-232.

Matsatsinis, N.F. and Samaras, A.P. (2000), “Brand choice model selection based on consumers’ multicriteria preferences and experts’ knowledge,” Computers & Operations Research, 27, pp.689-707.

Munier, B. (2001), “Risk attitudes appraisal and cognitive coordination in decentralized decision systems,” Group Decision and Negotiation, 10, pp.141-158.

Mustajoki J., Hämäläinen, R.P. and Lindstedt, M.R.K. (2006), “Using intervals for global sensitivity and worst-case analyses in multiattribute value trees,” European Journal of Operational Research, 174, pp.278-292.

Okoli, C. and Pawlowski, S.D. (2004), “The Delphi method as a research tool: an example, design considerations and applications,” Information & Management, 42, pp.15–29.

Osborn, A. F. (1942). How to think up. New York: McGraw-Hill.

Pöyhönen, M. and Hämäläinen, R.P. (1998), “Notes on the weighting biases in value trees,” Journal of Behavioral Decision Making, 11, pp.139-150.

Prelec, D. and Loewenstein, G. (1991), “Decision making over time and under uncertainty: a common approach,” Management Science, 37, pp.770-786.

Roy, B. and Bertier, B. (1981), “Lamerhode ELECTRE II: use methode de classmenr en presence de criterres multiples,” Direction Scientifique, working paper, 142.

Siskos, Y., Grigoroudis, E., Zopounidis, C. and Saurais, O. (1998), “Measuring customer satisfaction using a collective preference disaggregation model,” Journal of Global Optimization, 12, pp.175-195.
Tamura, H. (2005), “Behavioral models for complex decision analysis,” European Journal of Operational Research, 166, pp.655-665.

Timmermans, D. and Vlek, C. (1996), “Effects on decision quality of supporting multi-attribute evaluation in groups,” Organizational Behavior and Human Decision Processes, 68, pp.158-170.

Van Calker, K.J., Berentsen, P.B.M., Romero, C., Giesen, G.W.J. and Huirne, R.B.M. (2006), “Development and application of a multi-attribute sustainability function for Dutch dairy farming systems,” Ecological Economics, 57, pp.640-658.

Van den Honert, R.C. (2001), “Decisional power in group decision making: a note on the allocation of group members’ weights in the multiplicative AHP and SMART,” Group Decision and Negotiation, 10, pp.275-286.

Vlacic, L., Amagasa, M., Ishikawa, A., Williams, T.J. and Tomizawa, G. (1997), “Applying multiattribute-based group decision making techniques in complex equipment selection tasks,” Group Decision and Negotiation, 6, pp.529–556.

Von Neumann, J. and Morgenstern, O. (1944), Theory of Games and Economic Behavior, Princeton University Press, Princeton, NJ.

Von Nitzsch, R. and Weber, M. (1993), “The effect of attribute ranges on weights in multiattribute utility measurements,” Management Science, 39, pp.937-943.

Wang, Y.M., Yang, J.B. and Xu, D.L. (2005), “A preference aggregation method through the estimation of utility intervals,” Computers & Operations Research, 32, pp.2027-2049.

Wang, Y.M., Yang, J.B. and Xu, D.L. (2006), “Environmental impact assessment using the evidential reasoning approach,” European Journal of Operational Research, 174, pp.1885-1913.

Wei, Q.L., Ma, J. and Fan, Z.P. (2000), “A parameter analysis method for the weight-set to satisfy　preference　orders of alternatives in additive multi-criteria value　models,” Journal of Multi-Criteria Decision Analysis, 9, pp.181-190.

Xu, Z.S. and Da, Q.L. (2003), “An overview of operators for aggregating information,” International Journal of Intelligent Systems, 18, pp.953–969.

Yoon, K. and Kim, G. (1989), “Multiple attribute decision analysis with imprecise information,” IIE Transactions, 21, pp.21-26.