A Hesitant Fuzzy Linguistic TODIM Method Based on a Score Function

International Journal of Computational Intelligence Systems

Volumn 8, Issue 4, 2015, Pages 701-712

  Wei, Cuiping ^a,b   Ren, Zhiliang ^b   Rodríguez, Rosa M ^c  

^a YANGZHOU UNIVERSITY   (China)

^b QUFU NORMAL UNIVERSITY   (China)

^c UNIVERSITY OF GRANADA   (Spain)

Author keywords

Comparison operator; Distance measure; Hesitant fuzzy linguistic term set; Multi criteria decision making; Score function; TODIM method

Indexed keywords

BEHAVIORAL RESEARCH; LINGUISTICS;

COMPARISON OPERATORS; DISTANCE MEASURE; FUZZY LINGUISTICS; MULTI CRITERIA DECISION MAKING; SCORE FUNCTION; TODIM METHOD;

DECISION MAKING;

EID: 84930982828     PISSN: 18756891     EISSN: 18756883     Source Type: Journal    
DOI: 10.1080/18756891.2015.1046329     Document Type: Article

References (41)

1. M. Abdellaoui, H. Bleichrodt and C. Paraschiv, “Loss aversion under prospect theory: a parameter-free measurement,” Management Science, 53, 1659–1647 (2007).
2. I. Beg and T. Rashid, “TOPSIS for hesitant fuzzy linguistic term sets,” International Journal of Intelligent Systems, 28(12), 1162–1171 (2013).
3. C. Camerer, “Bounded rationality in individual decision making,” Experimental Economics, 1, 163–183 (1998).
4. M. Delgado, J.L. Verdegay and M.A. Vila, “On aggregation operations of linguistic labels,” International Journal of Intelligent Systems, 8(3), 351–370 (1993).
5. P.P. Bonissone and K.S. Decker, “Selecting uncertainty calculi and granularity: An experiment in trading-off precision and complexity,” Machine Intelligence and Pattern Recognition, 4, 217–247 (1986).
6. Z.P. Fan, X. Zhang, F.D. Chen and Y. Liu, “Extended TODIM method for hybrid multiple attribute decision making problems,” Knowledge-Based Systems, 42, 40–48 (2013).
7. L.F.A.M. Gomes and M.M.P.P. Lima, “From modeling individual preferences to multicriteria ranking of discrete alternatives: A look at Prospect Theory and the additive difference model,” Foundations of Computing and Decision Sciences, 17(3), 171–184 (1992).
8. L.F.A.M. Gomes and M.M.P.P. Lima, “TODIM: Basics and application to multicriteria ranking of projects with environmental impacts,” Foundations of Computing and Decision Sciences, 16(4), 113–127 (1992).
9. L.F.A.M. Gomes and L.A.D. Rangel, “An application of the TODIM method to the multicriteria rental evaluation of residential properties,” European Journal of Operational Research, 193(1), 204–211 (2009).
10. L.F.A.M. Gomes, L.A.D. Rangel and F.J.C. Maranhão, “Multicriteria analysis of natural gas destination in Brazil: An application of the TODIM method,” Mathematical and Computer Modelling, 50(1-2), 92–100 (2009).
11. F. Herrera and L. Martínez, “A 2-tuple fuzzy linguistic representation model for computing with words,” IEEE Transaction on Fuzzy Systems, 8, 746–752 (2000).
12. Z. Hu, Z. Chen, Z. Pei, X. Ma and W. Liu, “An improved ranking strategy for fuzzy multiple attribute group decision making,” International Journal of Computational Intelligence Systems, 6(1), 38–46 (2013).
13. C.L. Hwang and K. Yoon, “Multiple attribute decision making: Methods and applications: A state-of-the-art survey,” Springer-Verlag, (1981).
14. J. Kacprzyk and S. Zadrozny, “Computing with words is an implementable paradigm: Fuzzy queries, linguistic data summaries, and natural-language generation,” IEEE Transactions on Fuzzy Systems, 18(3), 461–472 (2010).
15. D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, 47, 263–292 (1979).
16. R.A. Krohling and T.M. de Souza, “Combining prospect theory and fuzzy numbers to multi-criteria decision making,” Expert Systems with Applications, 39(13), 11487–11493 (2012).
17. L.W. Lee and S.M. Chen, “Fuzzy decision making based on hesitant fuzzy linguistic term sets,” in: A. Selamat (Eds.) Intelligent Information and Database Systems, Part I LNAI7802, 21–30 (2013).
18. C.C. Li and Y. Dong, “Multi-attribute group decision making methods with proportional 2-tuple linguistic assessments and weights” International Journal of Computational Intelligence Systems, 7(4), 758–770 (2014).
19. H.C. Liao, Z.S. Xu and X.J. Zeng, “Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making,” Information Sciences, 271(1), 125–142 (2014).
20. H.B. Liu and R.M. Rodríguez, “A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making,” Information Sciences, 258, 220–238 (2014).
21. H.B. Liu, J.F. Cai and L. Jiang, “On improving the additive consistency of the fuzzy preference relations based on comparative linguistic expressions,” International Journal of Intelligent Systems, 29, 544–559 (2014).
22. R. Lourenzutti and R.A. Krohling, “A study of TODIM in a Intuitionistic fuzzy and random environment,” Expert Systems with Applications, 40(16), 6459–6468 (2013).
23. L. Martínez, “Sensory evaluation based on linguistic decision analysis,” International Journal of Aproximated Reasoning, 44(2), 148–164 (2007).
24. L. Martínez, D. Ruan and F. Herrera, “Computing with words in decision support systems: An overview on models and applications,” International Journal of Computational Intelligence Systems, 3(1), 382–395 (2010).
25. J.M. Mendel, L.A. Zadeh, R.R.Yager, J. Lawry, H. Hagras and S. Guadarrama, “What computing with words means to me,” IEEE Computational Intelligence Magazine, 5(1), 20–26 (2010).
26. A.C. Passos, M.G. Teixeira, K.C. Garcia, A.M. Cardoso and L.F.A.M. Gomes, “Using the TODIM-FSE method as a decision-making support methodology for oil spill response,” Computers and Operations Research, 42, 40–48 (2014).
27. W. Pedrycz and S. Mingli, “Analytic hierarchy process (AHP) in group decision making and its optimization with an allocation of information granularity,” IEEE Transactions on Fuzzy Systems, 19(3), 527–539 (2011).
28. W. Pedrycz, P. Ekel and R. Parreiras, “Fuzzy multicriteria decision-making: Models, methods and applications,” John Wiley and Sons, Ltd, (2011).
29. R.M. Rodríguez, L. Martínez and F. Herrera, “Hesitant fuzzy linguistic term sets,” in: Foundations of Intelligent Systems, 287–295 (2011).
30. R.M. Rodríguez, L. Martínez and F. Herrera, “Hesitant fuzzy linguistic term sets for decision making,” IEEE Transactions on Fuzzy Systems, 20(1), 109–119 (2012).
31. R.M. Rodríguez, L. Martínez and F. Herrera, “A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term set,” Information Sciences, 241, 28–42 (2013).
32. R.M. Rodríguez, L. Martínez, V. Torra, Z.S. Xu and F. Herrera, “Hesitant fuzzy sets: State of the art and future directions.” International Journal of Intelligent Systems, 29(6), 495–524 (2014).
33. A. Tversky and D. Kahneman, “Advances in prospect theory: cumulative representation of uncertainty,” Journal of Risk and Uncertainty, 5, 297–323 (1992).
34. J.Q. Wang, “An outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets,” Information Sciences, 280, 338–351 (2014).
35. Z.S. Xu, “Deviation measures of linguistic preference relations in group decision making,” Omega, 33(3), 249–254 (2005).
36. Y.M. Wang, J.B. Yang and D.L. Xu, “A preference aggregation method through the estimation of utility intervals,” Computers and Operations Research, 32, 2027–2049 (2005).
37. C.P. Wei, N. Zhao and X.J. Tang, “Operations and comparisons of hesitant fuzzy linguistic term sets,” IEEE Transactions of Fuzzy Systems, 22(3), 575–585 (2014).
38. D. Wu and J.M. Mendel, “Computing with words for hierarchical decision making applied to evaluating a weapon system,” IEEE Transactions Fuzzy Systems, 18(3), 441–460 (2010).
39. L.A. Zadeh, “The concept of a linguistic variable and its applications to approximate reasoning,” Information Sciences, Part I 8, 199–249; Part II 8, 301–357; Part III 9, 43–80, (1975).
40. X.L. Zhang and Z.S. Xu, “The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment,” Knowledge-Based Systems, 61, 48–58 (2014).
41. B. Zhu and Z.S. Xu, “Consistency measures for hesitant fuzzy linguistic preference relations,” IEEE Transactions on Fuzzy Systems, 22(1), 35–45 (2014).