現實生活中，人們常處在資訊不精確且多變的環境中，(Zadeh, 1965)定義模糊理論幫助人類描述並解決所遇到的問題。爾後有學者根據模糊理論的概念發展出直覺模糊理論，將人類心中實際感受與不確定的程度以數值表示，此理論一提出即引起許多學者投入研究並且應用在群體決策上，專家根據一些準則評估方案，尋求較佳的方案。找尋最佳決策，很常使用到相似性公式，利用公式計算專家意見集合的相似程度。然而，專家間的意見總會有分歧，可透過共識度流程，解決專家意見的分歧，整合出一致的意見，達成方案的共識性。
共識度流程是整合個人單一的意見為群體的意見，利用整合後的方案以數值方式進行排序。本研究廣泛蒐集不同的直覺模糊相似性測度公式，並使用共識度流程，將流程中定義的距離公式以不同的直覺模糊相似度公式替換，討論在不同的公式下，對共識度流程結果的影響。先針對公式計算的不同特性分成四組，再從這四組中選取方案排序結果彼此差異較大的公式進行實驗分析，並改變方案個數、準則個數及專家個數，討論在不同的情況下，不同的相似性公式其方案排序的情形，是否會影響決策。使用分析指標包括Spearman等級相關係數、一致率、反轉率及矛盾率，由結果發現，在改變不同的方案個數、準則個數及專家個數的情況下，方案個數的改變對於方案排序的影響甚大，方案個數愈多，愈容易改變方案排列，影響決策。相對的，準則個數與專家個數的改變，對方案排列的影響相當小，指標的曲線圖大致維持相同曲線。因此建議往後決策者在進行決策分析時，可以選擇容易計算且節省時間成本之公式即可。

In 1965, fuzzy sets were introduced by Zadeh to deal with the imprecise and variable data in real life. In several decades later, intuitionistic fuzzy set theory were developed by Atanassov to express the human feeling with numerical numbers, and were proposed in group decision making. One practical application is similarity measures. Naturally, at the beginning of every group decision making problem, experts’ opinions may differ substantially. Therefore, it is necessary to develop a consensus process in an attempt to obtain the maximum degree of consensus or agreement between the set of experts on the solution set of alternatives.
Consensus model process is processed to integrate a single opinion into group opinions, which could be ranked by numerical numbers. The aim of this paper is to present a consensus model for solving group decision making problems in intuitionistic fuzzy set environment. We conduct the proposed method on different similarity measures to discuss the effect to the ranking result. The similarity measures are separated into four groups from each similarity measure expression and its own measuring focus. In addition, a comprehensive experimental analysis to observe the intuitionistic fuzzy consensus results yielded by different similarity measures is presented. Several comparison indices are examined, including the average Spearman correlation coefficients, the consistency rate, the inversion rate, and the contradiction rate. According to the results, the four indices are affected as the number of decision alternatives in a problem increases. On the other hand, the number of attributes, and the number of experts have only a minor practical influence in view of the four indices. We suggest forward researchers that it might be a better choice to apply a simple and measure.

指導教授推薦書……………………………………………………………………….
口試委員會審定書……………………………………………………………………..
授權書…………………………………………………………………………….…..iii
誌謝…………………………………………………………………………………...iv
中文摘要 v
英文摘要.......................................................................................................................vi
目錄 vii
表目錄 ix
第一章 緒論 1
1.1研究動機與背景 1
1.2研究目的及內容 2
第二章 文獻探討 3
2.1 群體決策方法 3
2.2 模糊群體決策 4
2.3 直覺模糊理論 5
2.4 直覺模糊群體決策 6
2.5 共識性測度 8
第三章 直覺模糊相似性測度 10
3.1 直覺模糊集合基本概念 10
3.2 相似性測度公式 10
3.3 相似性測度公式比較 18
第四章 研究方法 36
4.1 直覺模糊群體決策共識度方法 36
4.2 直覺模糊群體決策共識度方法之數值例 39
第五章 實驗分析 42
5.1 分析指標與公式選取 42
5.2 Spearman等級相關係數分析 48
5.3 一致率分析(consistency rate) 49
5.4 反轉率分析(inversion rate) 50
5.5 矛盾率分析(contradiction rate) 51
5.6 實驗結果歸納 52
5.7 迴歸分析 52
第六章 結論與建議 70
6.1 結論 70
6.2 未來研究建議 71
參考文獻 72
附錄 77

許崇源(2001)，「我國非營利組織責任及透明度提昇之研究—德爾菲法之應用」，中山管理評論，第9卷，第4期，540-566頁。
李青芬、李雅婷、趙慕芬(2002)，組織行為學，台北市:華泰文化事業股份有限公司。.
鄧振源、曾國雄(1989)，「層級分析法(AHP)的內涵特性與應用(上)」，中國統計學報，第27卷，第6期，5-22頁。
Aczel, J., and Saaty, T. (1983), “Procedures for synthesizing ratio judgements,” Journal of Mathematical Psychology, Vol. 27, No. 1, pp. 93-102.
Atanassov, K. T. (1986), “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, Vol. 20, No. 1, pp. 87-96.
Atanassov, K. T., Pasi, G., and Yager, R. R. (2005), “Intuitionistic fuzzy interpretations of multi-criteria multi-person and multi-measurement tool decision making,” International Journal of Systems Science, Vol. 36, No. 14, pp. 859-868.
Barbarosoglu, G., and Yazgac, T. (1997), “An application of the analytic hierarchy process to the supplier selection problem,” Production and Inventory Management Journal, Vol. 38, No. 1, pp. 14-21.
Bordogna, G., Fedrizzi, M., and Pasi, G. (1997), “A linguistic modeling of consensus in group decision making based on OWA operators,” IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, Vol. 27, No. 1, pp. 126-132.
Bryson, N. (1996), “Group decision-making and the analytic hierarchy process: Exploring the consensus-relevant information content,” Computers and Operations Research, Vol. 23, No. 1, pp. 27-35.
Buckley, J. J. (1985), “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, Vol. 17, No. 3, pp. 233-247.
Chen, S. M. (1988), “A new approach to handling fuzzy decision making problems,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 18, No. 6, pp. 1012-1016.
Chen, S. M. (1997), “Similarity measures between vague sets and between elements,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 27, No. 1, pp. 153-158.
Chen, C. T. (2000), “Extensions of the TOPSIS for group decision-making under fuzzy environment,” Fuzzy Sets and Systems, Vol. 114, No. 1, pp. 1-9.
Chen, S. J., and Hwang, C. L. (1992), Fuzzy Multiple Attribute Decision Making, New York: Springer-Verlag.
Chen, S. M., and Tan, J. M. (1994), “Handling multicriteria fuzzy decision-making problems based on vague set theory,” Fuzzy Sets and Systems, Vol. 67, No. 2, pp. 163-172.
De, S. K., Biswas, R., and Roy, A. R. (2001), “An application of intuitionistic fuzzy sets in medical diagnosis,” Fuzzy Sets and Systems, Vol. 117, No. 2, pp. 209-213.
Delbeoq, A. L., Van de Ven, A. H., and Gustafson, D. H. (1975), Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes , New York: Scott Foresman and Company.
Foster, P. R., and Kozak, M. R. (1986), “Characteristics of a model industrial technology education field experience,” Technology Teacher, Vol. 46, No. 2, pp. 23-26.
Harsanyi, J. C. (1955), “Cardinal welfare, individualistic ethics, and interpersonal comparisons of utility,” Journal of Political Economy, Vol. 63, No. 1, pp. 309-321.
Herrera, F., Herrera-Viedma, E., and Chiclana, F. (2002), “A consensus model for multiperson decision making with different preference structures,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans. Vol. 32, No. 3, pp. 394-402.
Herrera, F., Herrera-Viedma, E., and Verdegay, J. L. (1996a), “Direct approach processes in group decision making using linguistic OWA operators,” Fuzzy Sets and Systems, Vol. 79, No. 2, pp. 175-190.
Herrera, F., Herrera-Viedma, E., and Verdegay, J. L. (1996b), “A model of consensus in group decision making under linguistic assessments,” Fuzzy Sets and Systems, Vol. 78, No. 1, pp. 73-87.
Hong, D. H., and Choi, C. H. (2000), “Multicriteria fuzzy decision-making problems based on vague set theory,” Fuzzy Sets and Systems, Vol. 114, No. 1, pp. 1915-1918.
Hong, D. H., and Kim, C. (1999), “A note on similarity measures between vague sets and between elements,” Information Sciences, Vol. 115, No. 1-4, pp. 83-96.
Hsu, H. M., and Chen, C. T. (1996), “Aggregation of fuzzy opinions under group decision making,” Fuzzy Sets and Systems, Vol. 79, No. 3, pp. 279-285.
Hung, D. H., and Hwang, S. Y. (1994), “A note on the value similarity of fuzzy systems variables,” Fuzzy Sets and Systems, Vol. 66, No. 3, pp. 383-386.
Hung, W. L., and Yang, M. S. (2004), “Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance,” Pattern Recognition Letters, Vol. 25, No. 14, pp. 1603-1611.
Hung, W. L., and Yang, M. S. (2008), “On similarity measures between intuitionistic fuzzy sets,” International Journal of Intelligent Systems, Vol. 23, No. 3, pp. 364-383.
Hwang, C. L., and Lin, M. J. (1987), Group Decision Making under Multiple Criteria-Methods and Applications, New York: Springer-Heidelberg.
Hwang, C. L., and Yoon, K. (1981), Multiple Attributes Decision Making Methods and Applications, New York: Springer-Verlag.
Ju, H. (2008), Study of Extended Interval-Valued Fuzzy Set and Application to Group Decision Consensus Degree, China: Chongqing.
Kacprzyk, J., and Fedrizzi, M. (1988), “A soft measure of consensus in the setting of partial (fuzzy) preferences,” European Journal of Operational Research, Vol. 34, No. 3, pp. 316-325.
Kacprzyk, J., Fedrizzi, M., and Nurmi, H. (1992), “Group decision making and consensus under fuzzy preferences and fuzzy majority,” Fuzzy Sets and Systems, Vol. 49, No. 1, pp. 21-31.
Krathwohl, D. R., (1993), Methods of Educational and Social Science Research: An Integrated Approach, New York: Longman.
Kuncheva, L. I., and Krishnapuram, R. (1996), “A fuzzy consensus aggregation operator,” Fuzzy Sets and Systems, Vol. 79, No. 3, pp. 347-356.
Lee, H. M. (1996), “Group decision making using fuzzy sets theory for evaluating the rate of aggregative risk in software development,” Fuzzy Sets and Systems, Vol. 80, No. 3, pp. 261-271.
Lee, H. S. (2002), “Optimal consensus of fuzzy opinions under group decision making environment,” Fuzzy Sets and Systems, Vol. 132, No. 3, pp. 305-315.
Li, D., and Cheng, C. (2002), “New similarity measures of intuitionistic fuzzy sets and application to pattern recognition,” Pattern Recognition Letters, Vol. 23, No. 1-3, pp. 221-225.
Li, T., and Ciyuan, X. (2007), “Research of group decision consensus degree based on extended intuitionistic fuzzy set,” in Cao, B. Y. (Ed.), Fuzzy Information and Engineering, New York: Springer Verlag, pp. 768-772.
Li, F., and Xu, Z. (2001), “Similarity measures between vague sets,” Journal of Software, Vol. 12, No. 6, pp. 922-927.
Li, Y., Chi, Z., and Yan, D. (2002), “Similarity measures between vague sets and vague entropy,” Journal of Computer Sciences, Vol. 29, No. 12, pp. 129-132.
Li, Y., Olson, D. L., and Qin, Z. (2007), “Similarity measures between intuitionistic fuzzy(vague) sets: A comparative analysis,” Pattern Recognition Letters, Vol. 28, No. 2, pp. 278-285.
Li, D. F., Wang, Y. C., and Liu, S., and Shan, F. (2009), “Fractional programming methodology for multi-attribute group decision-making using IFS,” Applied Soft Computing, Vol. 9, No. 1, pp. 219-225.
Liang, Z., and Shi, P. (2003), “Similarity measures on intuitionistic fuzzy sets,” Pattern Recognition Letters, Vol. 24, No. 15, pp. 2687-2693.
Linstone, H. A., and Turoff, M. (1975), The Delphi Method Techniques and Applications, London: Addison-Wesley.
Liu, H. W., and Wang, G. J. (2007), “Multi-criteria decision-making methods based on Intuitionistic fuzzy sets,” European Journal of Operational Research, Vol. 179, No. 1, pp. 220-233.
Mitchell, H. B. (2003), “On the Dengfeng-Chuntian similarity measure and its application to pattern recognition,” Pattern Recognition Letters, Vol. 24, No. 16, pp. 3101-3104.
Pappis, C. P., and Karacapilidis, N. I. (1993), “A comparative assessment of measures of similarity of fuzzy values,” Fuzzy Sets and Systems, Vol. 56, No. 2, pp. 171-174.
Ruoning, X., and Xiaoyan, Z. (1992), “Extensions of the analytic hierarchy process in fuzzy environment,” Fuzzy Sets and Systems, Vol. 52, No. 3, pp. 251-257.
Saaty, T. L. (1971), The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, New York: McGraw-Hill.
Saremik, M., Mousavi, S. F., and Sanayei, A. (2009), “TQM consultant selection in SMES with TOPSIS under fuzzy environment,” Expert Systems with Applications, Vol. 36, No. 2, pp. 2742-2749.
Szmidt, E., and Kacprzyk, J. (2004), “A concept of similarity for intuitionistic fuzzy sets and its use in group decision making,” Proceedings of IEEE International Conference on Fuzzy Systems, Vol. 2, No. 1, pp. 1129-1134.
Szmidt, E., and Kacprzyk, J. (2005), “A new concept of a similarity measure for intuitionistic fuzzy sets and its use in group decision making,” Lecture Notes in Computer Science,Vol. 3558, pp. 272-282.
Tanino, T. (1984), “Fuzzy preference orderings in group decision making,” Fuzzy Sets and Systems, Vol. 12, No. 2, pp. 117-131.
Tong, R. M., and Bonissone, P. P. (1980), “A linguistic approach to decision making with fuzzy sets,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 10, No. 11, pp. 716-723.
Tversky, A. (1977), “Features of similarity,” Psychological Review, Vol. 84, No. 4, pp. 327-352.
Wang, W. J. (1997), “New similarity measures on fuzzy sets and on elements,” Fuzzy Sets and Systems, Vol. 85, No. 3, pp. 305-309.
Wei, G. (2008), Induced Intuitionistic Fuzzy Ordered Weighted Averaging Operator and its Application to Multiple Attribute Group Decision Making, New York: Springer-Verlag Berlin Heidelberg.
Xie, B., Mi, J. S., and Han, L. W. (2008), “Entropy and similarity measure of intuitionistic fuzzy sets,” Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, China, ICMLC 1, art. No. 4620456, pp. 501-506.
Xu, Z. (2007a), “Intuitionistic fuzzy aggregation operators,” IEEE Transactions on Fuzzy Systems, Vol. 15, No. 6, pp. 1179-1197.
Xu, Z. (2007b), “Some similarity measures of intuitionistic fuzzy sets and their applications to multiple attribute decision making,” Fuzzy Optimization and Decision Making, Vol. 6, No. 2, pp. 109-121.
Xu, Z. (2007c), “Multi-person multi-attribute decision making models under intuitionistic fuzzy environment,” Fuzzy Optimization and Decision Making, Vol. 6, No. 3, pp. 221-236.
Yager, R. R. (1993), “Non-numeric multi-criteria multi-person decision making,” International Journal of Group Decision Making and Negotiation, Vol. 2, No. 1, pp. 81-93.
Yiu, C. Y., Ho, H. K., Lo, S. M., and Hu, B. Q. (2005), “Performance evaluation for cost estimators by reliability interval method,” Journal of Construction Engineering and Management, Vol. 131, No. 1, pp. 108-116.
Yusuff, R. M., Yee, K. P., and Hashmi, M. S. J. (2001), “A preliminary study on the potential use of the analytical hierarchical process (AHP) to predict advanced manufacturing technology (AMT) implementation,” Robotics and Computer Integrated Manufacturing, Vol. 17, No. 5, pp. 421-427.
Zadeh, L. A. (1965), “Fuzzy sets,” Information and Control, Vol. 8, No. 3, pp. 338-353.
Zadeh, L. A. (1975), “The concept of a linguistic variable and its application to approximate reasoning,” Information Sciences, Vol. 8, No. 3, pp. 199-249.
Zhang, C., and Fu, H. (2006), “Similarity measures on three kinds of fuzzy sets,” Pattern Recognition Letters, Vol. 27, No. 12, pp. 1307-1317.