簡易檢索 / 詳目顯示

回結果列表
研究生： HA THI XUAN CHI
HA - THI XUAN CHI
論文名稱： IMPROVED APPROACHES FOR RANKING GENERALIZED FUZZY NUMBERS AND FUZZY MUTIL-CRITERIA DECISION MAKING
IMPROVED APPROACHES FOR RANKING GENERALIZED FUZZY NUMBERS AND FUZZY MUTIL-CRITERIA DECISION MAKING
指導教授： 喻奉天
Vincent F. Yu
口試委員: 周碩彥
Shuo-Yan Chou
王孔政
Kung-Jeng Wang
郭人介
Ren-Jieh Kuo
吳建瑋
Chien-Wei Wu
陳振明
Jen-Ming Chen
張洝源
An-Yuan Chang
學位類別： 博士
Doctor
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 86
中文關鍵詞： Ranking fuzzy number 、Generalized fuzzy number 、Centroid 、Height 、Decision maker's optimism 、MCDM
外文關鍵詞： height, centroid, generalized fuzzy numbers, Ranking fuzzy number, decision maker’s optimism, MCDM.
相關次數： 點閱：1459  下載：5 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • Ranking fuzzy numbers, a significant component in decision making process, supports a decision maker in selecting the optimal solution. Althoung there are many existing ranking methods for fuzzy numbers, most of them suffer from some shortcomings. To overcome these shortcomings, this study proposes a new ranking approach for both normal and generalized fuzzy numbers that ensures full consideration of all information of fuzzy numbers. The proposed approach integrates the concept of centroid point, the left and the right (LR) areas between fuzzy numbers, height of a fuzzy number and the degree of decision maker’s optimism. Several numerical examples are presented to illustrate the efficiency and superiority of the proposed.
    To reduce uncertainty in decision making and avoid loss of information, this study also proposed a new fuzzy multi-criteria decision making (MCDM) approach based on the proposed ranking method for generalized fuzzy numbers. The applicability of the proposed fuzzy MCMD model is illustrated through a case study.

    ABSTRACT I ACKNOWLEDGEMENT III TABLE OF CONTENTS IV LIST OF TABLES VI LIST OF FIGURES VII CHAPTER 1. INTRODUCTION 1 1.1. Research background and motivation 1 1.2. Research objectives and contributions 5 1.3. Research framework 5 CHAPTER 2. FUNDAMENTALS 7 2.1. Classical sets (crisp sets) 7 2.2. Fuzzy sets 7 2.2.1. Terminology and Notation 7 2.2.2. Basic definitions 8 2.2.2.1 Linguistic variables: 9 2.2.2.2 Fuzzy arithmetic 9 2.2.2.3 Basic definitions of triangular and trapezoidal fuzzy numbers 10 2.2.2.4 Arithmetic operations for generalized fuzzy numbers 13 2.2.2.5 Basic properties of fuzzy quantities 14 CHAPTER 3. REVIEW OF EXISTING APPROACHES FOR RANKING FUZZY NUMBERS 15 3.1. Centroid approaches 15 3.2. Deviation degree approaches 17 3.2.1. Wang et al.’s approach 17 3.2.2. Nejad and Mashinchi’s approach 19 3.2.3. Asady’s approach 20 3.3. Magnitude approaches for ranking fuzzy numbers 21 3.4. Chen and Chen’s approaches 24 3.5. Kumar et al.’s approaches 26 CHAPTER 4. THE PROPOSED APPROACHES FOR RANKING FUZZY NUMBERS 30 4.1. The proposed epsilon-deviation degree approach 30 4.1.1. Shortcomings of deviation degree approaches 30 4.1.2. The proposed epsilon-deviation degree approach 34 4.1.3. Comparison the proposed epsilon degree approach with other existing approaches 38 4.2. The proposed approach for ranking generalized fuzzy numbers based on centroid and rank index 49 4.2.1. Shortcomings of Kumar et al.’s approach 49 4.2.2. The proposed approach 53 4.2.3. Comparison of the proposed approach with existing approaches 55 CHAPTER 5. THE PROPOSED FUZZY MCDM APPROACH 61 5.1. The proposed fuzzy MCDM approach 61 5.2. Ilustrated example 64 CHAPTER 6. CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS 68 6.1. Conclusions 68 6.2. Recommendations for further research 69 REFERENCE 71

    Abbasbandy, S., & Asady, B. (2006). Ranking of fuzzy numbers by sign distance. Information Sciences, 176(16), 2405-2416.
    Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers & Mathematics with Applications, 57(3), 413-419.
    Asady, B. (2010). The revised method of ranking lr fuzzy number based on deviation degree. Expert Systems with Applications, 37(7), 5056-5060.
    Asady, B. (2011). Revision of distance minimization method for ranking of fuzzy numbers. Applied Mathematical Modelling, 35(3), 1306-1313.
    Asady, B., & Zendehnam, A. (2007). Ranking fuzzy numbers by distance minimization. Applied Mathematical Modelling, 31(11), 2589-2598.
    Azadeh, A., Osanloo, M., & Ataei, M. (2010). A new approach to mining method selection based on modifying the nicholas technique. Applied Soft Computing, 10(4), 1040-1061.
    Bortolan, G., & Degani, R. (1985). A review of some methods for ranking fuzzy subsets. Fuzzy Sets and Systems, 15(1), 1-19.
    Chen, S.-H. (1985a). Operations on fuzzy numbers with function principal. Tamkang Journal of Management Sciences, 6, 13-25.
    Chen, S.-H. (1985b). Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets and Systems, 17(2), 113-129.
    Chen, S.-J., & Chen, S.-M. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Applied Intelligence, 26(1), 1-11.
    Chen, S.-M., & Chen, J.-H. (2009). Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert Systems with Applications, 36(3, Part 2), 6833-6842.
    Chen, S.-M., & Sanguansat, K. (2011). Analyzing fuzzy risk based on a new fuzzy ranking method between generalized fuzzy numbers. Expert Systems with Applications, 38(3), 2163-2171.
    Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95(3), 307-317.
    Cheng, C. H., & Mon, D. L. (1993). Fuzzy system reliability analysis by interval of confidence. Fuzzy Sets and Systems, 56(1), 29-35.
    Chou, S.-Y., & Chang, Y.-H. (2008). A decision support system for supplier selection based on a strategy-aligned fuzzy smart approach. Expert Systems with Applications, 34(4), 2241-2253.
    Chou, S.-Y., Chang, Y.-H., & Shen, C.-Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132-145.
    Chou, S.-Y., Dat, L. Q., & Yu, V. F. (2011a). A revised method for ranking fuzzy numbers using maximizing set and minimizing set. Computers & Industrial Engineering, 61(4), 1342-1348.
    Chou, S.-Y., Dat, L. Q., & Yu, V. F. (2011b). A revised method for ranking fuzzy numbers using maximizing set and minimizing set. Computers & Industrial Engineering, 61(4), 1342-1348.
    Chu, T. C., & Lin, Y. C. (2003). A fuzzy topsis method for robot selection. The International Journal of Advanced Manufacturing Technology, 21(4), 284-290.
    Chu, T. C., & Tsao, C. T. (2002). Ranking fuzzy numbers with an area between the centroid point and original point. Computers & Mathematics with Applications, 43(1-2), 111-117.
    Dagdeviren, M., Yavuz, S., & KilInc, N. (2009). Weapon selection using the ahp and topsis methods under fuzzy environment. Expert Systems with Applications, 36(4), 8143-8151.
    Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613-626.
    Ezzati, R., Allahviranloo, T., Khezerloo, S., & Khezerloo, M. (2012). An approach for ranking of fuzzy numbers. Expert Systems with Applications, 39(1), 690-695.
    Hajjari, T. (2011). On deviation degree methods for ranking fuzzy numbers. Australian Journal of Basic and Applied Sciences, 5(5), 750-758.
    Hanss, M. (2005). Applied fuzzy arithmetic. Springer-Verlag Berlin Heidelberg
    Jain, R. (1976). Decision making in the presence of fuzzy variables. IEEE Transactions on Systems, Man and Cybernetics, 6(10), 698-703.
    Kaufmann, A., & Gupta, M. (1991). Introduction to fuzzy arithmetic: Theory and applications ( 2nd ed ed.). New York: Van Nostrand Reinhold.
    Kaur, A., & Kumar, A. (2012). A new approach for solving fuzzy transportation problems using generalized trapezoidal fuzzy numbers. Applied Soft Computing, 12(3), 1201-1213.
    Kim, K., & Park, K. S. (1990). Ranking fuzzy numbers with index of optimism. Fuzzy Sets and Systems, 35(2), 143-150.
    Kumar, A., Singh, P., & Kaur, A. (2010). Ranking of generalized exponential fuzzy numbers using integral value approach. International Journal of Advances in Soft Computing and Its Applications, 2(2), 209-220.
    Kumar, A., Singh, P., Kaur, P., & Kaur, A. (2011a). A new approach for ranking of l–r type generalized fuzzy numbers. Expert Systems with Applications, 38(9), 10906-10910.
    Kumar, A., Singh, P., Kaur, P., & Kaur, A. (2011b). Rm approach for ranking of l–r type generalized fuzzy numbers. Soft Computing, 15(7), 1373-1381.
    Kuo, M.-S., & Liang, G.-S. (2012). A soft computing method of performance evaluation with mcdm based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476-485.
    Liou, T.-S., & Wang, M.-J. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50(3), 247-255.
    Ma, M., Friedman, M., & Kandel, A. (1999). A new fuzzy arithmetic. Fuzzy Sets and Systems, 108(1), 83-90.
    Mitchell, H. B., & Schaefer, P. A. (2000). On ordering fuzzy numbers. International Journal of Intelligent Systems, 15(11), 981-993.
    Nejad, A. M., & Mashinchi, M. (2011). Ranking fuzzy numbers based on the areas on the left and the right sides of fuzzy number. Computers & Mathematics with Applications, 61(2), 431-442.
    Tseng, M.-L. (2010). An assessment of cause and effect decision-making model for firm environmental knowledge management capacities in uncertainty. Environmental Monitoring and Assessment, 161(1), 549-564.
    Tseng, M.-L. (2011a). Green supply chain management with linguistic preferences and incomplete information. Applied Soft Computing, 11(8), 4894-4903.
    Tseng, M.-L. (2011b). Using a hybrid mcdm model to evaluate firm environmental knowledge management in uncertainty. Applied Soft Computing, 11(1), 1340-1352.
    Wang, M. L., Wang, H. F., & Lin, C. L. (2005). Ranking fuzzy numbers based on lexicographic screening procedure. International Journal of Information Technology and Decision Making, 4(4), 663-678.
    Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (i). Fuzzy Sets and Systems, 118(3), 375-385.
    Wang, Y.-J., & Lee, H.-S. (2008). The revised method of ranking fuzzy numbers with an area between the centroid and original points. Computers & Mathematics with Applications, 55(9), 2033-2042.
    Wang, Y.-M., & Luo, Y. (2009). Area ranking of fuzzy numbers based on positive and negative ideal points. Computers & Mathematics with Applications, 58(9), 1769-1779.
    Wang, Y.-M., Yang, J.-B., Xu, D.-L., & Chin, K.-S. (2006). On the centroids of fuzzy numbers. Fuzzy Sets and Systems, 157(7), 919-926.
    Wang, Z. X., Liu, Y. J., Fan, Z. P., & Feng, B. (2009). Ranking l-r fuzzy number based on deviation degree. Information Sciences, 179(13), 2070-2077.
    Xiao, Z., Xia, S., Gong, K., & Li, D. (2012). The trapezoidal fuzzy soft set and its application in mcdm. Applied Mathematical Modelling, 36(12), 5844-5855.
    Xiaowei, H., & Hepu, D. (2011). An area-based approach to ranking fuzzy numbers in fuzzy decision making. Journal of Computational Information Systems, 7(9), 3333- 3342.
    Yager, R., R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161.
    Yager, R. R. (1977). Multiple objective decision-making using fuzzy sets. International Journal of Man-Machine Studies, 9(4), 375-382.
    Yager, R. R. (1978, Jan. 1978). Ranking fuzzy subsets over the unit interval. Proceedings of the Decision and Control including the 17th Symposium on Adaptive Processes, 1978 IEEE Conference on (Vol. 17, pp. 1435-1437).
    Yager, R. R. (1980). On a general class of fuzzy connectives. Fuzzy Sets and Systems, 4(3), 235-242.
    Yong, D. (2006). Plant location selection based on fuzzy topsis. The International Journal of Advanced Manufacturing Technology, 28(7-8), 839-844.
    Yu, V. F., Chi, H. T. X., Dat, L. Q., Phuc, P. N. K., & Shen, C.-w. (2013a). Ranking generalized fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and areas. Applied Mathematical Modelling, 37(16–17), 8106-8117.
    Yu, V. F., Chi, H. T. X., & Shen, C.-w. (2013b). Ranking fuzzy numbers based on epsilon-deviation degree. Applied Soft Computing, 13(8), 3621-3627.
    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.
    Zimmermann, H. J. (1991). Fuzzy set theory and its applications. Dordrecht: Kluwer Academic Press.

    QR CODE