PL EN


2014 | 15 | 2 | 112-124
Article title

THE RATING SCALE MODEL IN THE CONSTRUCTION OF FUZZY TOPSIS METHOD

Content
Title variants
Languages of publication
EN
Abstracts
EN
Fuzzy TOPSIS method enables linear ordering of objects characterized by linguistic variables, which values constitute expressions emerging from natural language. Crucial, however, often neglected phase of this method is a selection of the way of introducing linguistic expressions by fuzzy numbers. Therefore, in this article one suggested a modification of fuzzy TOPSIS method using Rating Scale Model (RSM) to establish triangular fuzzy numbers. A suggested method enables establishing the rank of objects on the basis of objective criteria and subjective weights expressed in the form of triangular fuzzy numbers. Usability of the suggested method was confirmed by an empirical example, concerning linear ordering of selected smartphones models.
Year
Volume
15
Issue
2
Pages
112-124
Physical description
Dates
published
2014
Contributors
  • Department of Econometrics and Computer Science Wrocław University of Economics
References
  • Andrich D. (1978) A rating formulation for ordered response categories, “Psychometrika”, vol. 43, pp. 561-573.
  • Ataei E. (2013) Application of TOPSIS and Fuzzy TOPSIS Methods for Plant Layout Design, “World Applied Sciences Journal”, vol. 24, iss. 7, pp. 908-913.
  • Chang S.-H., Tseng H.-E. (2008) Fuzzy Topsis Decision Method for Configuration Management, “International Journal of Industrial Engineering”, vol. 15, iss. 3, pp. 304-313.
  • Chen C.-T. (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment, “Fuzzy Sets and Systems”, no. 114, pp. 1-9.
  • Ding J.F., Liang G.S. (2005) Using fuzzy MCDM to select partners of strategic alliances for linear shipping, “Information Sciences”, vol 1-3, pp. 197-225.
  • Erdoğan M., Bilişik Ö.N., Kaya İ., Baraçh H. (2013) A customer satisfaction model based on fuzzy TOPSIS and SERVQUAL methods, “Lecture Notes in Management Science”, vol. 5, pp. 74-83.
  • Hwang C.L., Yoon K. (1981) Multiple Attributes Decision Making Methods and Applications, Springer, Berlin Heidelberg.
  • Iron A. (1998) Fuzzy rules and fuzzy functions: A combination of logic and arithmetic operations for fuzzy numbers, “Fuzzy Sets and Systems”, vol. 99, iss. 1, pp. 49-56.
  • Linacre J.M. (2010) Transitional categories and usefully disordered thresholds, “Online Educational Research Journal”, pp. 1–10.
  • Madi E.N., A.O.M Tap (2011) Fuzzy TOPSIS Method in the Selection of Investment Boards by Incorporating Operational Risks, “Proceedings of the World Congress on Engineering”, vol. 1, pp. 291-295.
  • Opricovic S., Tzeng G. (2003) Defuzzification within a multicriteria decision model, “International Journal of Uncertainty Fuzziness and Knowledge-Based Systems”, vol. 11, no. 5, pp. 635-652.
  • Special report: Telephones, Skąpiec.pl, January 2014.
  • Rasch G. (1960) Probabilistic Models for Some Intelligence and Attainment Tests, Danish Institute for Educational Research, Copenhagen (Expanded edition, University of Chicago Press, 1980).
  • Shih H.-S., Shyur H.-J., Lee E.S. (2007) An extension of TOPSIS for group decision making, “Mathematical and Computer Modelling”, vol. 45, no. 7, pp. 801-813.
  • Uyun S., Riadi I. (2011) A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection, “Telkomnika”, vol.9, no.1, pp. 37-46.
  • Wysocki F. (2010) Metody taksonomiczne w rozpoznawaniu typów ekonomicznych rolnictwa i obszarów wiejskich, Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu, Poznań.
  • Yayla A.Y., Yildiz A., Özbek A. (2012) Fuzzy TOPSIS Method in Supplier Selection and Application in the Garment Industry, “FIBRES & TEXTILES in Eastern Europe”, vol. 20, no. 4, pp. 20-23.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-78554b52-f72a-4320-abaa-01f540957a65
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.