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研究生: 藍子鈞
Tzu-Chun Lan
論文名稱: 根據TOPSIS方法及直覺模糊集合之相似度測量以作多準則決策之新方法
Multicriteria Decision Making Based on the TOPSIS Method and Similarity Measures Between Intuitionistic Fuzzy Sets
指導教授: 陳錫明
Shyi-Ming Chen
口試委員: 李惠明
Huey-Ming Lee
呂永和
Yung-Ho Leu
程守雄
Shou-Hsiung Cheng
學位類別: 碩士
Master
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 141
中文關鍵詞: 直覺模糊集合直覺模糊數多準則決策相似度測量TOPSIS法
外文關鍵詞: Intuitionistic Fuzzy Sets, Intuitionistic Fuzzy Values, Multicriteria Decision Making, Similarity Measures, TOPSIS Method
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  • 根據直覺模糊集合以作多準則決策是一個重要的研究課題。近年來,已有一些根據直覺模糊集合以作多準則決策之方法被提出。在本論文中,首先我們提出一個根據直角三角模糊數的中心點的轉換技術以測量直覺模糊集合之相似度的新方法並且證明其符合相似度測量的特性。然後,我們提出一個根據TOPSIS法及直覺模糊集合之相似度測量以作模糊多準則決策之新方法,其能夠解決已存在之方法在某些情況下由於除以零而不能得到方案間之偏好排序的缺點。最後,我們用一些例子以說明我們所提之新的多準則決策方法可以克服已存在之方法的缺點。我們所提之方法提供我們一個有用的方法以根據直覺模糊集合以作模糊多準則決策問題。


    Multicriteria decision making based on intuitionistic fuzzy sets is an important research topic. In recent years, some methods have been presented for multicriteria decision making based on intuitionistic fuzzy sets. In this thesis, we propose a new similarity measure between intuitionistic fuzzy sets based on the centroid points of the transformed right-angled triangular fuzzy numbers and prove some properties of the proposed similarity measure between intuitionistic fuzzy values. Then, we propose a new multicriteria decision making method based on the TOPSIS method and the proposed similarity measure between intuitionistic fuzzy sets to overcome the drawbacks of the existing methods, where the existing methods have the drawbacks that they cannot get the preference order of alternatives in some situations due to the fact that they have “the division by zero problem”. We also use some examples to illustrate the proposed multicriteria decision making method can overcome the drawbacks of the existing methods. The proposed multicriteria decision making method provides us with a useful way for multicriteria decision making in intuitionistic fuzzy environments.

    Abstrct in Chinese.........................................................i Abstrct in English........................................................ii Acknowledgements.........................................................iii Contents..................................................................iv List of Figures and Tables................................................vi Chapter 1 Introduction.....................................................1 1.1 Motivation............................................................1 1.2 Related Literature....................................................2 1.3 Organization of This Thesis...........................................7 Chapter 2 Preliminaries....................................................8 2.1 Intuitionistic Fuzzy Sets.............................................8 2.2 Properties of Similarity Measures Between Intuitionistic Fuzzy Sets ...........................................................................9 2.3 Summary...............................................................9 Chapter 3 A Review of Existing Methods for Fuzzy Multicriteria Decision Making Based on Intuitionistic Fuzzy Sets.................................10 3.1 Joshi and Kumar’s Method.............................................10 3.2 Wang and Wei’s Method................................................13 3.3 Wu and Chen’s Method.................................................15 3.3 Summary..............................................................20 Chapter 4 A Novel Similarity Measure Between Intuitionistic Fuzzy Sets...21 4.1 A Review of Existing Similarity Measures Between Intuitionistic Fuzzy Sets......................................................................21 4.2 A New Similarity Measure Between Intuitionistic Fuzzy Sets............33 4.3 Pattern Recognition Applications......................................52 4.4 Summary...............................................................61 Chapter 5 A New Method for Multicriteria Decision Making Based on TOPSIS Method and the Proposed Similarity Measure Between Intuitionistic Fuzzy Sets ..........................................................................62 5.1 A New Multicriteria Decision Making Method Based on the TOPSIS Method and the Proposed Similarity Measure Between Intuitionistic Fuzzy Sets.....62 5.2 Application Examples..................................................65 5.3 Summary..............................................................122 Chapter 6 Conclusions....................................................123 6.1 Contributions of This Thesis.........................................123 6.2 Future Research......................................................123 References...............................................................125

    [1] K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87-96, 1986.
    [2] K. T. Atanassov, “New operators defined over the intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 61, no. 2, pp. 137-142, 1994.
    [3] L. Baccour, A. M. Alimi, and R. I. John, “Similarity measures for intuitionistic fuzzy sets: State of the art,” Journal of Intelligent & Fuzzy Systems, vol. 24, no. 1, pp. 37-49, 2013.
    [4] G. Beliakov, M. Pagola, and T. Wilkin, “Vector valued similarity measures for Atanassov’s intuitionistic fuzzy sets,” Information Sciences, vol. 280, pp. 352-367, 2014.
    [5] R. Benayoun, B. Roy, and B. Sussman, “ELECTRE: Une me´thode pour guider le choix en pre´sence de points de vue multiples,” SEMA-METRA International, Direction Scientifique, Note de travail 49, 1966.
    [6] F. E. Boran and D. Akay, “A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition,” Information Sciences, vol. 255, pp. 45-57, 2014.
    [7] H. Bustince and P. Burillo, “Vague sets are intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 79, no. 3, pp. 403-405, 1996.
    [8] T. Chaira, “A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images,” Applied Soft Computing, vol. 11, no. 2, pp. 1711-1717, 2011.
    [9] L. H. Chen, C. C. Hung, and C. C. Tu, “Considering the decision maker’s attitudinal character to solve multi-criteria decision making problems in an intuitionistic fuzzy environment,” Knowledge-Based Systems, vol. 36, pp. 129-138, 2012.
    [10] S. M. Chen, “Similarity measures between vague sets and between elements,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 27, no. 1, pp. 153-158, 1997.
    [11] S. M. Chen and C. H. Chang, “A novel similarity measure between Atanassov’s intuitionistic fuzzy sets based on transformation techniques with applications to pattern recognition,” Information Sciences, vol. 291, pp. 96-114, 2015.
    [12] S. M. Chen and Y. Randyanto, “A novel similarity measure between intuitionistic fuzzy sets and its applications,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, no. 7, pp. 1350021-1-1350021-34, 2013.
    [13] S. M. Chen and J. M. Tan, “Handling multicriteria fuzzy decision-making problems based on vague set theory,” Fuzzy Sets and Systems, vol. 67, no. 2, pp. 163-172, 1994.
    [14] S. H. Cheng, S. M. Chen, and T. C. Lan, “A new similarity measure between intuitionistic fuzzy sets for pattern recognition based on the centroid points of transformed fuzzy numbers,” in Proceedings of 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.
    [15] T. Y. Chen, “A note on distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric,” Fuzzy Sets and Systems, vol. 158, no. 22, pp. 2523-2525, 2007.
    [16] T. Y. Chen, “Bivariate models of optimism and pessimism in multi-criteria decision-making based on intuitionistic fuzzy sets,” Information Sciences, vol. 181, no. 11, pp. 2139-2165, 2011.
    [17] T. Y. Chen, “A comparative analysis of score functions for multiple criteria decision making in intuitionistic fuzzy settings,” Information Sciences, vol. 181 , no. 17, pp. 3652-3676, 2011.
    [18] S. K. De, R. Biswas, and A. R. Roy, “An application of intuitionistic fuzzy sets in medical diagnosis,” Fuzzy Sets and Systems, vol. 117, no. 2, pp. 209-213, 2001.
    [19] L. Dengfeng and C. Chuntian, “New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions,” Pattern Recognition Letters, vol. 23, no. 1-3, pp. 221-225, 2002.
    [20] L. Dymova and P. Sevastjanov, “Operations on intuitionistic fuzzy values in multiple criteria decision making,” Scientific Research of the Institute of Mathematics and Computer Science, vol. 10, no. 1, pp. 41-48, 2011.
    [21] L. Fan and X. Zhangyan, “Similarity measures between vague sets,” Journal of Software, vol. 12, no. 6, pp. 922-927, 2001.
    [22] W. L. Gau and D. J. Buehrer, “Vague sets,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 2, pp. 610-614, 1993.
    [23] T. Geng, A. Zhang, and G. Lu, “Consensus intuitionistic fuzzy group decision-making method for aircraft cockpit display and control system evaluation,” Systems Engineering and Electronics, vol. 24, no. 4, pp. 634-641, 2013.
    [24] P. Grzegorzewski, “Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric,” Fuzzy Sets and Systems, vol. 148, no. 2, pp. 319-328, 2004.
    [25] K. Guo and W. Li, “An attitudinal-based method for constructing intuitionistic fuzzy information in hybrid MADM under uncertainty,” Information Sciences, vol. 208, pp. 28-38, 2012.
    [26] Z. Guo, M. Qi, and X. Zhao, “A new approach based on intuitionistic fuzzy set for selection of suppliers,” in Proceedings of the 2010 Sixth International Conference on Natural Computation, Yantai, Shandong, China, 2010, vol. 7, pp. 3715-3718.
    [27] Y. He, H. Chen, Z. He, and L. Zhou, “Multi-attribute decision making based on neutral averaging operators for intuitionistic fuzzy information,” Applied Soft Computing, vol. 27, pp. 64-76, 2015.
    [28] Y. He, H. Chen, L. Zhou, and J. Liu, and Z. Tao, “Intuitionistic fuzzy geometric interaction averaging operators and their application to multi-criteria decision making,” Information Sciences, vol. 259, pp. 142-159, 2014.
    [29] D. H. Hong and C. H. Choi, “Multicriteria fuzzy decision-making problems based on vague set theory,” Fuzzy Sets and Systems, vol. 114, no. 1, 103-113, 2000.
    [30] D. H. Hong and C. Kim, “A note on similarity measures between vague sets and between elements,” Information Sciences, vol. 115, no. 1-4, pp. 83-96, 1999.
    [31] W. L. Hung and M. S. Yang, “Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance,” Pattern Recognition Letters, vol. 25, no. 14, pp. 1603-1611, 2004.
    [32] W. L. Hung and M. S. Yang, “On similarity measures between intuitionistic fuzzy sets,” International Journal of Intelligent Systems, vol. 23, no. 3, pp. 364-383, 2008.
    [33] C. M. Hwang, M. S. Yang, W. L. Hung, and M. G. Lee, “A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition,” Information Sciences, vol. 189, pp. 93-109, 2012.
    [34] I. Iancu, “Intuitionistic fuzzy similarity measures based on Frank t-norms family,” Pattern Recognition Letters, vol. 42, pp. 128-136, 2014.
    [35] D. Joshi and S. Kumar, “Intuitionistic fuzzy entropy and distance measure based on TOPSIS method for multi-criteria decision making,” Egyptian Informatics Journal, vol. 15, no. 2, pp. 97-104, 2014.
    [36] A. Kaufmann and M. M. Gupta, “Fuzzy Mathematical Models in Engineering and Management Science,” Elsevier Science Publishers B.V., Amsterdam, The Netherlands, 1988.
    [37] Y. J. Lai, T. Y. Liu, and C. L. Hwang, “TOPSIS for MODM,” European Journal of Operational Research, vol. 76, no. 3, pp. 486-500, 1994.
    [38] J. Li, G. Deng, H. Li, and W. Zeng, “The relationship between similarity measure and entropy of intuitionistic fuzzy sets,” Information Sciences, vol. 188, pp. 314-321, 2012.
    [39] J. Li and C. Zhang, “A new solution of intuitionistic fuzzy multiple attribute decision-making based on attributes preference,” in Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, 2009, vol. 3, pp. 228-232.
    [40] M. Li, L. Jin, and J. Wang, “A new MCDM method combining QFD with TOPSIS for knowledge management system selection from the user’s perspective in intuitionistic fuzzy environment,” Applied Soft Computing, vol. 21, pp. 28-37, 2014.
    [41] Y. Li, D. L. Olson, and Z. Qin, “Similarity measures between intuitionistic fuzzy (vague) sets: A comparative analysis,” Pattern Recognition Letters, vol. 28, no. 2, pp. 278-285, 2007.
    [42] Y. Li, C. Zhongxian, and Y. Degin, “Similarity measures between vague sets and vague entropy,” J. Computer Sci., vol. 29, no. 12, pp. 129-132, 2002.
    [43] Z. Liang and P. Shi, “Similarity measures on intuitionistic fuzzy sets,” Pattern Recognition Letters, vol. 24, no. 15, pp. 2687-2693, 2003.
    [44] H. B. Mitchell, “On the Dengfeng-Chuntian similarity measure and its application to pattern recognition,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3101-3104, 2003.
    [45] G. A. Papacostas, A. G. Hatzimichaillidis, and V. G. Kaburlasos, “Distance and similarity measures between intuitionistic fuzzy sets: A comparative analysis from a pattern recognition point view,” Pattern Recognition Letters, vol. 34, no. 14, pp. 1609-1622, 2013.
    [46] Z. Pei and L. Zheng, “A novel approach to multi-attribute decision making based on intuitionistic fuzzy sets,” Expert Systems with Applications, vol. 39, no. 3, pp. 2560-2566, 2012.
    [47] B. Roy, “Classement et choix en presence de points de vue multiples (lamethode ELECTRE),” RIRO, vol. 8, pp. 57-75, 1968.
    [48] E. Szmidt and J. Kacprzyk, “Distances between intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 114, no. 3, pp. 505-518, 2000.
    [49] C. Tan and X. Chen, “Intuitionistic fuzzy Choquet integral operator for multi-criteria decision making,” Expert Systems with Applications, vol. 37, no. 1, pp. 149-157, 2010.
    [50] H. Wang and G. Wei, “An effective supplier selection method with intuitionistic fuzzy information,” in Proceedings of the 2008 International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China, 2008, pp. 1-4.
    [51] H. Y. Wang and S. M. Chen, “Artificial intelligence approach to evaluate students’ answerscripts based on the similarity measure between vague sets,” Educational Technology & Society, vol. 10, no. 4, pp. 224-241, 2007.
    [52] W. Wang and X. Liu, “Intuitionistic fuzzy geometric aggregation operators based on Einstein operations,” International Journal of Intelligent Systems, vol. 26, no. 11, pp. 1049-1075, 2011.
    [53] W. Wang and X. Xin, “Distance measure between intuitionistic fuzzy sets,” Pattern Recognition Letters, vol. 26, no. 13, pp. 2063-2069, 2005.
    [54] W. Wang, Z. Xu, S. S. Liu, and J. Tang, “A netting clustering analysis method under intuitionistic fuzzy environment,” Applied Soft Computing, vol. 11, no. 8, pp. 5558-5564, 2011.
    [55] Z. Wang, Z. Xu, S. Liu, and Z. Yao, “Direct clustering analysis based on intuitionistic fuzzy implication,” Applied Soft Computing, vol. 23, pp. 1-8, 2014.
    [56] J. Wu, F. Chen, C. Nie, and Q. Zhang, “Intuitionistic fuzzy-valued Choquet integral and its application in multicriteria decision making,” Information Sciences, vol. 222, no. 10, pp. 509-527, 2013.
    [57] J. Wu and Q. Zhang, “Multicriteria decision making method based on Intuitionistic fuzzy weighted entropy,” Expert Systems with Applications, vol. 38, no. 1, pp. 916-922, 2011.
    [58] J. Q. Wang and H. Y. Zhang, “Multicriteria decision-making approach based on Atanassov’s intuitionistic fuzzy sets with incomplete certain information on weights,” IEEE Transactions on Fuzzy Systems, vol. 2, no. 3, pp. 510-515, 2013.
    [59] M. C. Wu and T. Y. Chen, “The ELECTRE multicriteria analysis approach based on intuitionistic fuzzy sets,” in Proceedings of the 2009 IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 2009, pp. 1383-1388.
    [60] M. C. Wu and T. Y. Chen, “The ELECTRE multicriteria analysis approach based on Atanassov’s intuitionistic fuzzy sets,” Expert Systems with Applications, vol. 38, no. 10, pp. 12318-12327, 2011.
    [61] Y. Xu, Y. Wang, and X. Miu, “Multi-attribute decision making method for air target threat evaluation based on intuitionistic fuzzy sets,” Systems Engineering and Electronics, vol. 23, no. 6, pp. 891-897, 2012.
    [62] Z. Xu, “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, 2007.
    [63] Z. Xu, “Intuitionistic fuzzy aggregation operators,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 6, pp. 1179-1187, 2007.
    [64] Z. Xu, “Choquet integrals of weighted intuitionistic fuzzy information,” Information Sciences, vol. 180, no. 5, pp. 726-736, 2010.
    [65] Z. Xu, “Intuitionistic fuzzy multiattribute decision making: An interactive method,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 3, pp. 514-525, 2012.
    [66] Z. Xu, J. Chen, and J. Wu, “Clustering algorithm for intuitionistic fuzzy sets,” Information Sciences, vol. 178, no. 19, pp. 3775-3790, 2008.
    [67] Z. Xu and R. R. Yager, “Some geometric aggregation operators based on intuitionistic fuzzy sets,” International Journal of General Systems, vol. 35, no. 4, pp. 417-433, 2006.
    [68] Z. Xu and R. R. Yager, “Intuitionistic fuzzy Bonferroni means,” IEEE Transactions on Fuzzy Systems, vol. 41, no. 2, pp. 568-578, 2011.
    [69] Z. Xu and R. R. Yager, “Intuitionistic and interval-valued intuitionistic fuzzy preference relations and their measures of similarity for the evaluation of agreement within a group,” Fuzzy Optimization and Decision Making, vol. 8, no. 2, pp. 123-139, 2009.
    [70] D. Xu, Z. Xu, S. Liu, and H. Zhao, “A spectral clustering algorithm based on intuitionistic fuzzy information,” Knowledge-Based Systems, vol. 53, pp. 20-26, 2013.
    [71] J. Ye, “Cosine similarity measures for intuitionistic fuzzy sets and their applications,” Mathematical and Computer Modelling, vol. 53, no. 1-2, pp. 91-97, 2011.
    [72] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338-356, 1965.
    [73] H. Zhang and L. Yu, “New distance measures between intuitionitic fuzzy sets and interval-valued fuzzy sets,” Information Sciences, vol. 245, pp. 181-196, 2013.
    [74] H. Zhao, Z. Xu, S. S. Liu, and Z. Wang, “Intuitionistic fuzzy MST clustering algorithms,” Computers & Industrial Engineering, vol. 62, no. 4, pp. 1130-1140, 2012.
    [75] H. Zhao, Z. Xu, M. F. Ni, and S. Liu, “Generalized aggregation operators for intuitionistic fuzzy sets,” International Journal of Intelligent Systems, vol. 25, no.1, pp. 1-30, 2010.

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