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Fuzzy multiple criteria decision-making via an inverse function-based total utility approach

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Abstract

This work presents ranking alternatives under fuzzy multiple criteria decision making (MCDM) via an inverse function-based total utility approach, where ratings of alternatives versus qualitative criteria as well as importance weights of all criteria are assessed in linguistic values represented by fuzzy numbers. Membership functions of the final fuzzy values of alternatives can be developed; some of their properties are investigated and proved. The right utility is obtained from the inverse function of right membership function of the final fuzzy value and the inverse function of maximizing set, while the left utility is obtained from the inverse function of left membership function of the final fuzzy value and the inverse function of minimizing set. Total utility is the sum of the right and left utilities. A larger total utility indicates that the corresponding alternative is more favorable. The ranking of fuzzy numbers can be clearly formulated to increase the applicability of the suggested fuzzy MCDM model. A numerical example demonstrates the feasibility of the proposed method, and some comparisons are provided to reveal robustness and advantages of the proposed method.

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References

  • Akdag H, Kalayc T, Karagöz S, Zülfikar H, Giz D (2014) The evaluation of hospital service quality by fuzzy MCDM. Appl Soft Comput 23:239–248

    Article  Google Scholar 

  • Chen SH (1985) Ranking fuzzy numbers with maximizing set and minimizing set. Fuzzy Sets Syst 17(2):113–129

    Article  MathSciNet  Google Scholar 

  • Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making. Springer, Berlin

    Book  Google Scholar 

  • Chu TC, Charnsethikul P (2013) Ordering alternatives under fuzzy multiple criteria decision making via a fuzzy number dominance based ranking approach. Int J Fuzzy Syst 15(3):263–273

    MathSciNet  Google Scholar 

  • Çifçi G, Büyüközkan G (2011) A fuzzy MCDM approach to evaluate green suppliers. Int J Comput Intell Syst 4(5):894–909

    Article  Google Scholar 

  • Das S, Guha D (2016) A centroid-based ranking method of trapezoidal intuitionistic fuzzy numbers and its application to MCDM problem. Fuzzy Inf Eng 8(1):41–74

    Article  MathSciNet  Google Scholar 

  • Destercke S, Couso I (2015) Ranking of fuzzy intervals seen through the imprecise probabilistic lens. Fuzzy Sets Syst 278:20–39

    Article  MathSciNet  Google Scholar 

  • Dinagar DS, Kamalanathan S (2015) A method for ranking of fuzzy numbers using new area method. Int J Fuzzy Math Arch 9(1):61–71

    Google Scholar 

  • Dubois D, Prade H (1978) Operations on fuzzy numbers. Int J Syst Sci 9(6):613–626

    Article  MathSciNet  Google Scholar 

  • Duzce SA (2015) A new ranking method for trapezial fuzzy numbers and its application to fuzzy risk analysis. J Intell Fuzzy Syst 28(3):1411–1419

    MathSciNet  Google Scholar 

  • Gu Q, Xuan Z (2017) A new approach for ranking fuzzy numbers based on possibility theory. J Comput Appl Math 309:674–682

    Article  MathSciNet  Google Scholar 

  • Hari Ganesh A, Jayakumar S (2014) Ranking of fuzzy numbers using radius of gyration of centroids. Int J Basic Appl Sci 3(1):17–22

    Google Scholar 

  • Kahraman C (2008) Fuzzy multi-criteria decision making: theory and applications with recent developments. Springer, Berlin

    Book  Google Scholar 

  • Kaufmann A, Gupta MM (1991) Introduction to fuzzy arithmetic: theory and application. Van Nostrand Reinhold, New York

    MATH  Google Scholar 

  • Liou TS, Wang MJJ (1992) Ranking fuzzy numbers with integral value. Fuzzy Sets Syst 50(3):247–255

    Article  MathSciNet  Google Scholar 

  • Moghimi R, Anvari A (2014) An integrated fuzzy MCDM approach and analysis to evaluate the financial performance of iranian cement companies. Int J Adv Manuf Technol 71(1–4):685–698

    Article  Google Scholar 

  • Pedrycz W, Ekel P, Parreiras R (2010) Fuzzy multicriteria decision-making: theory, methods and applications. Wiley, London

    Book  Google Scholar 

  • Ribeiro RA (1996) Fuzzy multiple attribute decision making: a review and new preference elicitation techniques. Fuzzy Sets Syst 78(2):155–181

    Article  MathSciNet  Google Scholar 

  • Salehi K (2015) A hybrid fuzzy MCDM approach for project selection problem. Decis Sci Lett 4(1):109–116

    Article  Google Scholar 

  • Shen KY, Hu SK, Tzeng GH (2017) Financial modeling and improvement planning for the life insurance industry by a rough knowledge based hybrid MCDM model. Inf Sci 375:296–313

    Article  Google Scholar 

  • Torfi F, Farahani RZ, Mahdavi I (2016) Fuzzy MCDM for weight of object’s phrase in location routing problem. Appl Math Model 40(1):526–541

    Article  MathSciNet  Google Scholar 

  • Ülker B (2015) A fuzzy MCDM algorithm and practical decision aid tool to determine the best ROV design alternative. Kybernetes 44(4):623–645

    Article  Google Scholar 

  • Wang X, Kerre EE (2001a) Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets Syst 118(3):375–385

    Article  MathSciNet  Google Scholar 

  • Wang X, Kerre EE (2001b) Reasonable properties for the ordering of fuzzy quantities (II). Fuzzy Sets Syst 118(3):387–405

    Article  MathSciNet  Google Scholar 

  • Wang Z, Zhang-Westmant L (2014) New ranking method for fuzzy numbers by their expansion center. J Artif Intell Soft Comput Res 4(3):181–187

    Article  Google Scholar 

  • Wu Z, Ahmad J, Xu J (2016) A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information. Appl Soft Comput 42:314–324

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  • Zadeh LA (1975a) The concept of a linguistic variable and its application to approximate reasoning, part 1. Inf Sci 8(3):199–249

    Article  Google Scholar 

  • Zadeh LA (1975b) The concept of a linguistic variable and its application to approximate reasoning, part 2. Inf Sci 8(4):301–357

    Article  Google Scholar 

  • Zadeh LA (1975c) The concept of a linguistic variable and its application to approximate reasoning, part 3. Inf Sci 9(1):43–80

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the two anonymous referees, Prof. Genovese and Prof. Bruno for providing very helpful comments and suggestions. Their insights and comments led to a better presentation of the ideas expressed in this paper. This work was supported in part by Ministry of Science and Technology of the Republic of China, Taiwan, under Grant MOST 105-2410-H-218-002.

Funding

This study was funded in part by Ministry of Science and Technology of the Republic of China, Taiwan, under Grant MOST 105-2410-H-218-002.

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Correspondence to Ta-Chung Chu.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by A. Genovese, G. Bruno.

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Chu, TC., Yeh, WC. Fuzzy multiple criteria decision-making via an inverse function-based total utility approach. Soft Comput 22, 7423–7433 (2018). https://doi.org/10.1007/s00500-018-3167-0

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