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Abstract

This paper presents fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method for academic member selection. In academic member selection problem the ratings of various alternatives versus various subjective criteria and the weights of all criteria are assessed in linguistic variables represented by fuzzy numbers. Fuzzy numbers try to resolve the ambiguity of concepts that are associated with human being’s judgments. To determine the order of the alternatives, closeness coefficient is defined by calculating the distances to the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS). Universities can select the appropriate academic member by using fuzzy TOPSIS method. By this way the quality of education will be increased in universities.

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References

  1. Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Izadikhah, M., “Extensions of the TOPSIS method for decision-making problems with fuzzy data”, Applied Mathematics and Computation, 2006, Article in press.

    Google Scholar 

  2. Nur Jumaadzan, Z. M., Jacob, K. D., “Faculty member selection: a comparative study of AHP and its variants”, MCDM 2004, Whistler, B. C. Canada, August 6-11, 2004.

    Google Scholar 

  3. Hwang, C.L., Yoon, K., “Multiple Attributes Decision Making Methods and Applications”, Springer, Berlin Heidelberg, 1981.

    Google Scholar 

  4. Benitez, J.M., Martin, J.C., Roman, C., “Using fuzzy number for measuring quality of service in the hotel industry”, Tourism Management, Article in press.

    Google Scholar 

  5. Wang, M.Y., Elhag, T.M.S., “Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment”, Expert Systems with Applications, 2006, 31, 309-319.

    Article  Google Scholar 

  6. Chen, C.T., “Extensions of the TOPSIS for group decision-making under fuzzy environment”, Fuzzy Sets and Systems, 2000, 114, 1-9.

    Google Scholar 

  7. Saghafian, S., Hejazi, A.R., “Multi-criteria group decision making using a modified fuzzy TOPSIS procedure”, Proceedings of the 2005 International Conference on Computational Intelligence for Modeling, Control and Automation, and Conference Intelligent Agents, Web Technologies and Internet Commerce, 2005 IEEE.

    Google Scholar 

  8. Tsaur, S.H, Chang, T.Y, Yen, C.H., “The evaluation of airline service quality by fuzzy MCDM”, Tourism Management, 2002, 23,107-115.

    Article  Google Scholar 

  9. Zadeh, L.A., “Fuzzy Sets”, Information and Control, 1965, 8, 338-353.

    Article  MATH  Google Scholar 

  10. Chen, G., Pham, T.T., “Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems” CRC Press, Florida, 2001.

    Google Scholar 

  11. Ertuğrul, Í, Karakaşoğlu N., “The fuzzy analytic hierarchy process for supplier selection and an application in a textile company, Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, May 29-31, 2006, 195-207.

    Google Scholar 

  12. Bojadziev, G., Bojadziev, M., “Fuzzy Sets, Fuzzy Logic, Applications, World Scientific Publishing, Singapore, 1998.

    Google Scholar 

  13. Deng, H., ‘Multicriteria analysis with fuzzy pair-wise comparison”, International Journal of Approximate Reasoning, 1999, 21, 215-231.

    Article  Google Scholar 

  14. Baykal, N., Beyan, T., Bulanı k Mantık ÍIke ve Temelleri, Bı çaklar Kitabevi, Ankara, 2004.

    Google Scholar 

  15. Chen, C.T., Lin, C.T., Huang, S.F., “A fuzzy approach for supplier evaluation and selection in supply chain management”, International Journal of Production Economics, 2006, 102, 289–301.

    Article  Google Scholar 

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Ertuğrul, Í., Karakaşoğlu, N. (2007). Fuzzy TOPSIS Method for Academic Member Selection in Engineering Faculty. In: Iskander, M. (eds) Innovations in E-learning, Instruction Technology, Assessment, and Engineering Education. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6262-9_27

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  • DOI: https://doi.org/10.1007/978-1-4020-6262-9_27

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-6261-2

  • Online ISBN: 978-1-4020-6262-9

  • eBook Packages: EngineeringEngineering (R0)

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