The fuzzy TOPSIS method and its implementation in the R programme

• Authors: Dudek Andrzej , Jefmański Bartłomiej

Title variants

PL

Rozmyta metoda TOPSIS i jej implementacja w programie R

• Languages of publication: PL EN

Abstracts

EN

The TOPSIS method (Technique for Order Preference by Similarity Ideal Solu-tion) suggested by Hwang and Yoon [1981], belongs to the group of pattern linear ordering methods of multidimensional objects. A characteristic feature of this method is a way to evaluate a synthetic criterion’s values, which takes into consideration the distance of an evaluated object from a positive-ideal solution as well as from a negative-ideal solution. The fuzzy TOPSIS method enables the linear ordering of objects described through linguistic variables, whose values are expressed in the form of triangular fuzzy numbers. In this arti-cle, a way of synthetic measurement estimation in environment R was presented, according to the assumptions of the fuzzy TOPSIS method proposed by Chen [2000]. Scripts, which are included in the article make the accomplishment of this particular method’s stages pos-sible.

Keywords

EN

linear ordering; fuzzy TOPSIS; fuzzy number; linguistic variable; R programme

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Informatyka Ekonomiczna
• Year: 2015
• Issue: 1(35)
• Pages: 19-27

Contributors

author

Dudek Andrzej

author

Jefmański Bartłomiej

References

• 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.
• Błoński K., Jefmański B., 2013, Determinants of satisfaction of the employees of local government units, “Economics & Sociology”, vol. 6, no. 2, pp. 158-170.
• 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.
• 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 Attribute Decision Making. Methods and Applications, Sprin- ger, Berlin.
• Hellwig Z., 1968, Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze wzglę-du na poziom ich rozwoju oraz zasoby i strukturę wykwalifikowanych kadr, „Przegląd Staty-styczny”, nr 15, pp. 307-327.
• Jannatifar H., Shahi M.K.K., Moradi J.S., 2012, Assessing intellectual capital management by fuzzy TOPSIS, “Decision Science Letters”, vol. 2, iss. 6, pp. 1991-2000.
• Jefmański B., 2015, Zastosowanie modeli IRT w konstrukcji rozmytego system wag dla zmiennych w zagadnieniu porządkowania liniowego – na przykładzie metody TOPSIS, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu (w druku).
• Kia S.H., Danaei A., Oroei M., 2014, An application of fuzzy TOPSIS on ranking products: A case study of faucet devices, “Decision Science Letters”, vol. 3, iss. 1, pp. 43-48.
• Madi E.N., Tap A.O.M., 2011, Fuzzy TOPSIS Method in the Selection of Investment Boards by In-corporating Operational Risks, Proceedings of the World Congress on Engineering, vol. 1, pp. 291-295.
• Uyun S., Riadi I., 2011, A Fuzzy TOPSIS multiple-attribute decision making for scholarship selection, “Telkomnika”, vol. 9, no. 1, pp. 37-46.
• Walesiak M., 2006, Uogólniona miara odległości w statystycznej analizie wielowymiarowej, Wydaw-nictwo Akademii Ekonomicznej we Wrocławiu, Wrocław.
• Wysocki F., 2010, Metody taksonomiczne w rozpoznawaniu typów ekonomicznych rolnictwa i obsza-rów 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.