Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

A Fuzzy AHP Model for Selection of University Academic Staff

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Daniel E. Asuquo, Friday E. Onuodu

Daniel E Asuquo and Friday E Onuodu. A Fuzzy AHP Model for Selection of University Academic Staff. International Journal of Computer Applications 141(1):19-26, May 2016. BibTeX

	author = {Daniel E. Asuquo and Friday E. Onuodu},
	title = {A Fuzzy AHP Model for Selection of University Academic Staff},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {1},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {19-26},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2016908969},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


There is a growing interest in university academic staff selection since the quality of staff has a direct influence on any organization’s effectiveness. The process of selecting suitable academic staff for employment is complex and involves taking multiple criteria into consideration for good decision making. Analytic Hierarchy Process (AHP) is a Multi-Criteria Decision Making (MCDM) model for dealing with decision making problems affected by several conflicting factors. It is useful for selecting the best among alternatives based on certain criteria. However, academic staff selection also contains uncertainties which pose another problem, since the AHP lacks the ability to deal with imprecise and subjective judgment in its pair-wise comparison process. This problem can be overcome by the use of AHP model with fuzzy logic, called Fuzzy AHP model, where triangular fuzzy numbers (TFNs) and linguistic variables are used to achieve better accuracy and consistency in the decision makers’ (DM) judgment. A system architecture is developed for problem solving using this model. This paper uses Chang’s synthetic extent analysis with TFNs to improve human experts’ decision making when recruiting by generating a range of values to incorporate DMs’ uncertainty, instead of a crisp value. Numerical example using three alternative candidates based on these criteria: work experience, academic background, and individual skill is presented. The result indicates that the alternative with the highest normalized weight is the most suitable candidate to be selected for employment. This work could be very useful to university establishment and to any other organization that may be interested in fair and efficient recruitment exercise.


  1. Rouyendegh, B. D. and Erkan, T. E. 2012. Selection of Academic Staff using the Fuzzy Analytical Hierarchy Process (FAHP): A Pilot Study, Tehnicki vjesnik, 19(4), 923-929.
  2. Mollaghasemi, M. 1997 Decisions, IEEE, California.
  3. Mendoza, G.A. and Martins, H. 2006. Multi-criteria decision analysis in natural resource management: A critical review of methods and new modeling paradigms, Forest Ecology and Management, 230 (1-3), 1 – 22.
  4. Ribeiro, R. A. 1996. Fuzzy multiple criterion decision making: A review and new preference elicitation techniques, Fuzzy Sets and Systems,78, 155-181.
  5. Kabir, G. and Hasin, M. A. A. 2011. Comparative analysis of AHP and fuzzy AHP models for multicriteria inventory classification, International Journal of Fuzzy Logic Systems, 1(1), (October, 2011), 1-16.
  6. Mahendran, P., Moorthy, M. B. K. and Saravanan, S. 2014. A fuzzy AHP approach for selection of measuring instrument for engineering college selection, Applied Mathematical Sciences, 8(44), 2149 – 2161.
  7. Singh A.P., Ghosh S.K. and Sharma, P. 2007. Water quality management of a stretch of river Yamuna: An interactive fuzzy multi-objective approach, Water Resource Management, 21,515–532.
  8. Bajić, D. and Polomčić, D. 2014. Fuzzy optimization in hydrodynamic analysis of groundwater control systems: case study of the pumping station “Bezdan 1”, Serbia, Annales Geologiques de la Peninsule Balkanique, 75, (December, 2014), 103-110.
  9. Mikhailov, L. and Tsvetinov, P. 2004. Evaluation of Services using a fuzzy analytic hierarchy process, Applied Soft Computing, 5, 23–33.
  10. Dubey, S. K., Mittal, A. and Rana, A. 1998. Measurement of object oriented software usability using fuzzy AHP, International Journal of Computer Science and Telecommunications, 3(5), 98-104.
  11. Liang, S. and Wang, M. J. J. 1994. Personnel selection using fuzzy MCDM algorithm, European Journal of Operational Research, 78, 22–33.
  12. Asuquo, D. E. and Umoh, U. A. 2015. Analytic hierarchy process for QoS evaluation of mobile data networks, International Journal of Computer Networks and Communications, 7(6), (November, 2015), 125-137.
  13. Saaty, T.L. 1980. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York.
  14. Saaty, T. L. 2008. Decision Making with the Analytic Hierarchy Process, International Journal of Services Sciences, 1(1), 83-98.
  15. Zadeh, L. A. 1965. Fuzzy Sets. Information and Control, 8, 338–353.
  16. Zadeh, L.A. 1975. The Concept of a Linguistic Variable and its Application to Approximate Reasoning, Information Sciences, 8, 199–249.
  17. Shu, M. S., Cheng, C. H. and Chang, J. R. 2006. Using Intuitionistic Fuzzy Set for Fault-tree Analysis on Printed Circuit Board Assembly, Microelectronics Reliability, 46 (12), 2139-2148.
  18. Zhu, K. J., Jing, Y. and Chang, D. Y., 1999. A Discussion on Extent Analysis Method and Applications of Fuzzy AHP, European Journal of Operational Research, 116(2), 4450-456.
  19. van Laarhoven, P. J. M. and Pedrycz, W. 1983. A fuzzy Extension of Saaty’s Priority Theory, Fuzzy Sets and Systems, 11, 229–241.
  20. Buckley, J. J. 1985. Fuzzy Hierarchical Analysis, Fuzzy Sets and Systems, 17(3), 233–247.
  21. Chang, D. Y. 1996. Applications of the Extent Analysis Method on Fuzzy AHP, European Journal of Operational Research, 95(3), 649–655.
  22. Xu, R., 2006. Fuzzy Least Square Priority Method in the Analytic Hierarchy Process, Fuzzy Sets and Systems, 112(3), 395-404.
  23. Mikhailov, L. 2003. Deriving Priorities from Fuzzy Pair-wise Comparison Judgements, Fuzzy Sets and Systems, 134(3), 365-385.
  24. Wang, Y. M., Yang, J. B. and Xu, D. L. 2005. A Two-stage Logarithmic Goal Programming Method for Generating Weights from Interval Comparison Matrices, Fuzzy Sets Systems, 152, 475-498.
  25. Deng, H. 1999. Multicriteria Analysis with Fuzzy Pairwise Comparisons, International Journal of Approximate Reasoning, 21, 215–231.
  26. Tolga, E., Demircan, M. L. and Kahraman, C. 2005. Operating System Selection Using Fuzzy Replacement Analysis and Analytic Hierarchy Process, International Journal of Production Economics, 97, 89-117.
  27. Chang, D. Y. 1992. Extent Analysis and Synthetic Decision Optimization Techniques and Applications, vol.1, World Scientific, Singapore, 352.
  28. Bozbura, F. T., Beskese, A., and Kahraman, C. 2007. Prioritization of Human Capital Measurement Indicators using FAHP, Expert Systems with Applications, 32(4), 1100–1112.


MCDM, Staff Selection Process, Fuzzy AHP, Triangular Fuzzy Numbers & Synthetic Extent Analysis