Open Access
Issue
SHS Web of Conferences
Volume 25, 2016
ICITCE 2015 – 3rd International Conference on Information Technology and Career Education
Article Number 02024
Number of page(s) 7
Section Economy and technology
DOI https://doi.org/10.1051/shsconf/20162502024
Published online 22 April 2016
  1. Kahraman, C. & Cebi, S. 2009. A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Expert Systems with Applications. 36: 4848–4861. [CrossRef]
  2. Chen, T. 2012. Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights. Applied Mathematical Modelling, 36: 3029–3052. [CrossRef]
  3. Z.S. Xu & J. Chen. 2007. An interactive method for fuzzy multiple attributes group decision making. Inform Sciences, 177: 248–263. [CrossRef]
  4. Z.J. Wang, K.W. Li & W.Z. Wang. 2009. An approach to multiple attribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights. Inform Sciences, 179: 3026–3040. [CrossRef]
  5. J. Ye. 2010. Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. Eur J Oper Res, 205: 202–204. [CrossRef]
  6. G.W. Wei. 2010. Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Know Inf Syst, 25: 623–634. [CrossRef]
  7. D. Molodtsov. 1999. Soft set theory-first results. Comput Math Appl, 37: 19–31. [CrossRef]
  8. P.K. Maji & A.R. Roy. 2002. An application of soft sets in a decision making problem. Comput Math Appl, 44: 1077–1083. [CrossRef]
  9. M.M. Mushrif, S. Sengupta & A.K. Ray. 2006. Texture classification using a novel, soft-set theory based classification algorithm. COMPUTER VISION-ACCV 2006, pp.246–254. [CrossRef]
  10. Y. Zou & Z. Xiao. 2008. Data analysis approaches of soft sets under incomplete information. Knowl-Based Syst, 21: 941–945. [CrossRef]
  11. A.R. Roy & P.K. Maji. 2007. A fuzzy soft set theoretic approach to decision making problems. J Comput Appl Math, 203: 412–418. [CrossRef]
  12. Z. Kong, L.Q. Gao & L.F. Wang. 2009. Comment on “A fuzzy soft set theoretic approach to decision making problems”. J Comput Appl Math, 223: 540–542. [CrossRef]
  13. N. Cagman & S. Enginoglu. 2010. Soft set theory and uni-int decision making. Eur J Oper Res, 207: 848–855. [CrossRef]
  14. F. Feng, Y.B. Jun, X. Liu & L. Li. 2010. An adjustable approach to fuzzy soft set based decision making. J Comput Appl Math, 234: 10–20. [CrossRef]
  15. S.J. Chen & C.L. Hwang. 1992. Fuzzy Multiple Attribute Decision Making: Method and Applications, New York: Springer-Verlag.
  16. T.L. Saaty. 1996. Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Priorization of Complexity. Pittsburgh: RWS Publications.
  17. T.L. Saaty & L.G. Vargas. 1998. Diagnosis with dependent symptoms: Bayes theorem and the analytic hierarchy process. Oper Res, 46: 491–502. [CrossRef]
  18. R. Yua & G. Tzeng. 2006. A soft computing method for multi-criteria decision making with dependence and feedback. Appl Math Computation, 180: 63–75. [CrossRef]
  19. B. Kosko. 1996. Fuzzy Engineering, New York: Prentice Hall.
  20. Z. Xu. 2007. A method for multiple attribute decision making with incomplete weight information in linguistic setting. Knowl-Based Syst, 20: 719–725. [CrossRef]
  21. D. Park, Y. Kwun, J. Park & I. Park. 2009. Correlation coefficient of interval-valued intuitionistic fuzzy sets and its application to multiple attribute group decision making problems. Math Comput Model, 50: 1279–1293. [CrossRef]
  22. S. French, R. Hartley, L.C. Thomas & D.J. White. 1983. Multi-objective Decision Making, New York: Academic Press.
  23. Y.T. Lin et al. 2010. A novel hybrid MCDM approach for outsourcing vendor selection: A case study for a semiconductor company in Taiwan. Expert Syst Appl, 37: 4796–4804. [CrossRef]

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