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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
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.
Hwang, C.L., Yoon, K., “Multiple Attributes Decision Making Methods and Applications”, Springer, Berlin Heidelberg, 1981.
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.
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.
Chen, C.T., “Extensions of the TOPSIS for group decision-making under fuzzy environment”, Fuzzy Sets and Systems, 2000, 114, 1-9.
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.
Tsaur, S.H, Chang, T.Y, Yen, C.H., “The evaluation of airline service quality by fuzzy MCDM”, Tourism Management, 2002, 23,107-115.
Zadeh, L.A., “Fuzzy Sets”, Information and Control, 1965, 8, 338-353.
Chen, G., Pham, T.T., “Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems” CRC Press, Florida, 2001.
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.
Bojadziev, G., Bojadziev, M., “Fuzzy Sets, Fuzzy Logic, Applications, World Scientific Publishing, Singapore, 1998.
Deng, H., ‘Multicriteria analysis with fuzzy pair-wise comparison”, International Journal of Approximate Reasoning, 1999, 21, 215-231.
Baykal, N., Beyan, T., Bulanı k Mantık ÍIke ve Temelleri, Bı çaklar Kitabevi, Ankara, 2004.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer
About this paper
Cite this paper
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
Download citation
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)