An approach to give grade for each departments of a university based on their performances

Main Article Content

Sadigh Raissi

Abstract

Decide on the most efficient department could help university managers to conduct their decision in a proper manner especially in their resource allocation programs, encouraging decision support systems, quality metrics and so on. Different decision maker may use different information bases, unlike relative weights to their criteria and also different uncertainty levels in their expressions. They could utilize many quantitative or qualitative methods based on their knowledge and experiences. There is no unique and commonly accepted procedure to accomplish such decision properly. This paper tends to advise a systematic approach to handle such management problems. Through this paper readers will be familiar with a simple theory and easy of use method called Analytic Hierarchy Process (AHP), the way to compare different departments in a given university based on their commonly experts expression and a sample hierarchy structure for the such desired cases. This method called Fuzzy AHP and abbreviated FAHP later. In order of more warning to the prescribed efficient method, we avoided to deliver detail arithmetical calculations.

Article Details

How to Cite
Raissi, S. (2015). An approach to give grade for each departments of a university based on their performances. The Journal of Quality in Education, 5(6BIS), 7. https://doi.org/10.37870/joqie.v5i6BIS.113
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References

[1]- Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
[2]- Chen S.J. and Hwang, C.L. (1992). Fuzzy Multiple Attribute Decision Making: Methods and Applications, Lecture Notes in Economics and Mathematical Systems, Springer Verlage, Berlin.
[3]- Amy H.I. Lee et al. (2006). A fuzzy AHP and BSC approach for evaluating the performance of IT department in the manufacturing industry in Taiwan, Expert Systems with Applications, article on press.
[4]- Basak, I. and Saaty, T.L. (1993), Group decision making using the analytic hierarchy process, Mathematical and Computer Modeling, 17, 233-247.
[5]- Basak, I. (1997). Rank-based statistical procedures in analytic hierarchy process, European Journal of Operational Research, 101, 39-50.
[6]- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338"“353.
[7]- Tsaur, S. H., Tzeng, G. H., & Wang, K. C. (1997). Evaluating tourist risks from fuzzy perspectives. Annals of Tourism Research, 24(4), 796"“812.
[8]- Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism Management, 23, 107"“115.
[9]- Gupta, M. M., Saridis, G. N., & Gaines, B. R. (1977). Fuzzy automata and decision processes. New York: Elsevier North-Holland.
[10]- Liang, G. S., & Wang, M. J. (1994). Personnel selection using fuzzy MCDM algorithm. European Journal of Operational Research, 78, 22"“33.
[11]- Leung, L.C. and Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP, European Journal of Operation Research, Vol. 124.
[12]- Saaty, T. L. (1994). How to make a decision: the analytic hierarchy process. Interfaces, 24(6),
19"“43.
[13]- Van Laarhoven, P. J. M. and Pedrycs, W. (1993). A Fuzzy extension of Saatyes priority theory, Fuzzy Sets and Systems, Vol. 11.
[14]- Buckley, J.J. (1985). Fuzzy hierarchical analysis, Fuzzy Sets and Systems, Vol. 17.
[15]- Csutora, R., & Buckley, J. J. (2001). Fuzzy hierarchical analysis: the Lambda-Max method. Fuzzy Sets and Systems, 120, 181"“195.
[16]- Chen, C. T. (2000). Extensions of TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, 114, 1"“9.