理想解相似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution; TOPSIS) 是一種常被決策者使用之多屬性決策方法，其透過衡量可行方案與理想解之距離，將所有可行方案進行排序，以求得最佳方案。直覺式模糊數(Intuitionistic fuzzy number)包含正、負向資訊及猶豫資訊，若將其應用於TOPSIS決策模式上，能較傳統直接給予明確數值或模糊數的方式得到更多資訊，讓所得到之評估值更能貼近專家們的實際想法。隨著決策問題範圍擴大與複雜性增加，需要多位決策者才能做適當的分析，此外，在TOPSIS決策過程中，專家、屬性權重與正、負理想解之衡量方式眾說紛紜。因此，本研究將建構出應用於直覺式模糊環境下之群體TOPSIS決策方法，提高決策結果的真實性與合理性。
本研究將使用直覺式模糊數作為評估值，並將決策模式分為兩個部分。初始整合階段為尋找參與決策之專家，接著透過專家集體討論的方式，共同決定評估屬性與可行方案，並各自對方案與屬性進行評估，然後利用熵值(Entropy)求得個別專家之權重，再進行專家意見整合；直覺式模糊TOPSIS決策階段則利用整合後之決策矩陣計算各屬性之權重，並將屬性權重與專家意見整合，然後進行TOPSIS決策程序，先使用雙重兩極性決定正、負理想解，再利用投影法對可行方案進行排序，找到最佳方案。最後以本研究之決策模式進行範例演算，分別與Boran et al.(2009)和Xu and Hu(2010)進行比較與分析，其中包含敏感度分析與穩健性之測試，最後得知本研究之決策模式能得到較合適之決策結果，以供未來決策者參考。

Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) is a kind of multiple-attribute decision-making method often used by decision-makers. If intuitionistic fuzzy numbers, which contain positive, negative, and hesitation information are applied to a TOPSIS decision-making model, more information can be obtained and the evaluation results can be closer to the actual ideas of experts. In addition, since decision-making problems have become more complex, multiple decision-makers are required to make an appropriate analysis. Therefore, this study constructs a group TOPSIS decision-making method for an intuitionistic fuzzy environment to improve the authenticity and rationality of decision-making results.
This research adopts intuitionistic fuzzy numbers in a two-phase decision-making model.In the initial integration phase, experts collectively decide and then evaluate the attributes and alternatives. Then, entropy is used to obtain the weights of individual experts, and expert opinions are integrated. In the intuitionistic fuzzy TOPSIS decision-making phase, an integrated decision matrix is used to calculate the weights of individual attributes. The attribute weights are integrated with expert opinions, and then dual bipolar are used to determine the positive and negative ideal solutions. The projection method is adopted to rank the alternatives and find the best solution. Two examples are presented to illustrate the procedure of the proposed method and to compare the sorting results obtained using various models.

摘要........I
誌謝.......VI
目錄......VII
表目錄.......VIII
圖目錄......X
第一章 緒論.......1
第一節 研究背景與動機.....1
第二節 研究目的.....3
第三節 研究限制.....4
第四節 研究流程.....4
第五節 論文架構.....5
第二章 文獻探討.....7
第一節 直覺式模糊集合理論....7
第二節 理想解相似度順序偏好法(TOPSIS)...19
第三節 直覺式模糊TOPSIS群體決策(IF-TOPSIS)..24
第四節 利用投影法之群體決策....32
第五節 小結.....34
第三章 直覺式模糊TOPSIS之群體決策模式....35
第一節 研究構想......35
第二節 模式建構與求解.....38
第三節 小結......45
第四章 範例演算......46
第一節 範例一 : 多屬性決策問題....46
第二節 範例二 : 群體多屬性決策問題...53
第三節 小結......65
第五章 結論與未來研究方向.....66
第一節 研究結論......66
第二節 未來研究方向.....67
參考文獻......68
附錄.......73

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
Aloini, D., Dulmin, R. and Mininno, V. (2014) A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Systems with Applications, 41, 2157-2165.
Aloini, D., Dulmin, R., Mininno, V., Pellegrini, L. and Farina, G. (2018). Technology assessment with IF-TOPSIS: An application in the advanced underwater system sector. Technological Forecasting ＆ Social Change, 131, 38-48.
Burillo, P. and Bustince, H. (1996). Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Fuzzy Sets and Systems, 78(3), 305-316.
Boran, F. E., Genç, S., Kurt, M. and Akay, D. (2009). A muiti-criteria intuitionistic fuzzy
group decision making for supplier selection with TOPSIS method. Expert Systems
with Applications, 36(8), 11363-11368.
Chen, C. T. (2000). Extension of the TOPSIS for group decision-making under fuzzy
environment. Fuzzy sets and Systems, 114(1), 1-9.
Chen, T. Y. (2011). A comparative analysis of score functions for multiple criteria decision
Making in intuitionistic fuzzy settings. Information Sciences, 181,3652-3676.
Chen, S. M. and Tan, J. M. (1994). Handling multi-criteria fuzzy decision-making problems
based on vague set theory. Fuzzy sets and Systems, 67(2), 163-172.
Chen, L. H. and Tu, C. H. (2014). Dual bipolar measures of Atanassov’s intuitionistic fuzzy
sets. IEEE Transactions on Fuzzy Systems, 22(4), 966-982.
Chen, S. M., Cheng, S. H. and Lan, T. C. (2016). Multi-criteria decision making based on the TOPSIS method and similarity measures between intuitionistic fuzzy values. Information Sciences, 367, 279-295.
De Luca, A. and Termini, S. (1972). A defim=nition of a non-probabilistic entropy in the setting of fuzzy sets theory. Information and Control, 20, 301-312.
Ghyym, S. H. (1999). A semi-linguistic fuzzy approach to multi-actor decision-making:
Application to aggregation of expert’s judgements. Annals of Nuclear Energy, 26(1),
1097-1112.
Grzegorzewski, P. (2004). Distances between intuitionistic fuzzy sets and/or
interval-valued fuzzy sets based on the Hausdorff metric. Fuzzy Sets and Systems, 148, 319-328.
Gumus, S., Kucukvar, M. and Tatari, O. (2016). Intuitionistic fuzzy multi-criteria decision making framework based on life cycle environmental, economic and social impacts: The case of U.S. wind energy. Sustainable Production and Consumption, 8, 78-92.
Herrera, F., Herrera-Viedma, E. and Chiclana, F. (2000). A fusion approach for managing
Multi-granularity linguistic term sets in decision making. Fuzzy sets and Systems,
114(1), 43-58.
Hong, D. H. and Choi, C. H. (2000). Multi-criteria fuzzy decision-making problems based
On vague set theory. Fuzzy sets and Systems, 114(1), 103-113.
Hwang, C. L. and Yoon, K. (1981). Multiple Attributes Decision Making: Methods and
Applications: Springer, Berlin Heidelberg.
Izadikhah, M. (2009). Using the Hamming distance to extended TOPSIS in a fuzzy environment. Journal of Computational and Applied Mathematics, 231, 200-207.
Joshi, D. and Kumar, S. (2014) Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making. Egyptian Informatics Journal, 15, 97-104.
Jahanshahloo, G. R., Hosseinzadeh Lotfi, F. and Izadikhah, M. (2006b). Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Mathematics and Computation, 181, 1544-1551.
Kucukvar, M., Gumus, S., Egilmez, G. and Tatari, O. (2014) Ranking the sustainability performance of pavements: An intuitionistic fuzzy decision making method. Automation in Construction, 40, 33-43.
Ning, X., Dinga, L. Y., Luo, H. B. and Qi, S. J. (2016). A multi-attribute model for construction site layout using intuitionistic fuzzy logic. Automation in Construction, 72, 380-387.
Onata, N. C., Gumusb, C., Kucukvarc, M. and Tatarid, O. (2016). Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies. Sustainable Production and Consumption, 6, 12-25.
Roghanian, E., Rahimi, J., and Ansari, A. (2010). Comparison of first aggregation and last
aggregation in fuzzy group TOPSIS. Applied Mathematical Modelling, 34(12), 3754-3766.
Ren, P., Xu, Z., Liao, H., Zeng, X. J. (2017). A thermodynamic method of intuitionistic fuzzy MCDM to assist the hierarchical medical system in China. Information Sciences, 420, 490–504.
Shang, X. G. and Jiang, W. S. (1997). A note on fuzzy information measures. Pattern Recognition Letters, 18, 425-432.
Szmidt, E. and Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 505-518.
Szmidt, E. and Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy Sets and Systems, 118(3), 467-477.
Shih, H. S., Shyur, H. J. and Lee, E.S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7), 801-813.
Tooranloo, H. S. and Ayatollahba, A. S. (2016). A model for failure mode and effects analysis based on intuitionistic fuzzy approach. Applied Soft Computing, 49, 238-247.
Tsaur, S. H., Chang, T. Y. and Yen, C. H. (2002). The evaluation of airline service quality
by fuzzy MCDM. Tourism Management, 23, 107-115.
Vlachos, I. K. and Sergiadis, G. D. (2007). Intuitionistic fuzzy information - Applications to pattern recognition. Pattern Recognition Letters, 28, 197-206.
Vahdani, B. et al. (2013). A new design of the elimination and choice translating reality
Method for multi-criteria group decision-making in an intuitionistic fuzzy
environment. Applied Mathematical Modeling, 37(4), 1781-1799.
Wang, Y., Liu, J., Li. J. and Niu, C. (2016). An integrating OWA-TOPSIS framework in intuitionistic fuzzy settings for multiple attribute decision making. Computers ＆ Industrial Engineering, 98, 185-194.
Xu, Z. (2005). An overview of methods for determining OWA weights. International Journal of Intelligent Systems, 20(8), 843-865.
Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179-1187.
Xu, Z. and Hu, H. (2010). Projection models for intuitionistic fuzzy multiple attribute decision making. International Journal of Information Technology ＆ Decision Making, 09(02), 267-280.
Yue, Z. (2012). Approach to group decision making based on determining the weights of experts by using projection method. Applied Mathematical Modelling, 36(7), 2900-2910.
Yue, Z. (2013). An intuitionistic fuzzy projection-based approach for partner selection.
Applied Mathematical Modelling, 37(23), 9538-9551.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate
reasoning-I. Information Sciences, 8(3), 199-249.
Zhang, X., Jin, F. and Liu, P. (2013). A grey relational projection method for multi-attribute
decision making based on intuitionistic trapezoidal fuzzy number. Applied Mathematical Modelling, 37(5), 3467-3477.