Advanced SearchSearch Tips
A Cascaded Fuzzy Inference System for University Non-Teaching Staff Performance Appraisal
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Cascaded Fuzzy Inference System for University Non-Teaching Staff Performance Appraisal
Neogi, Amartya; Mondal, Abhoy Chand; Mandal, Soumitra Kumar;
  PDF(new window)
Most organizations use performance appraisal system to evaluate the effectiveness and efficiency of their employees. In evaluating staff performance, performance appraisal usually involves awarding numerical values or linguistic labels to employees performance. These values and labels are used to represent each staff achievement by reasoning incorporated in the arithmetical or statistical methods. However, the staff performance appraisal may involve judgments which are based on imprecise data especially when a person (the superior) tries to interpret another person`s (his/her subordinate) performance. Thus, the scores awarded by the appraiser are only approximations. From fuzzy logic perspective, the performance of the appraisee involves the measurement of his/her ability, competence and skills, which are actually fuzzy concepts that can be captured in fuzzy terms. Accordingly, fuzzy approach can be used to handle these imprecision and uncertainty information. Therefore, the performance appraisal system can be examined using Fuzzy Logic Approach, which is carried out in the study. The study utilized a Cascaded fuzzy inference system to generate the performance qualities of some University non-teaching staff that are based on specific performance appraisal criteria.
Performance Appraisal;Cascaded Fuzzy Inference System;University Non-Teaching Staff;Sensitivity Analysis;Gaussian MF;Fuzzy Rules;
 Cited by
A defuzzification-free hierarchical fuzzy system (DF-HFS): Rock mass rating prediction, Fuzzy Sets and Systems, 2017, 307, 50  crossref(new windwow)
M. Armstrong, A. Baron, Performance Management: The New Reality, London: Institute of Personnel and Development, 1998.

F.A. III. Koslowski, "Quality and Assessment in Context: A Brief Review", Quality Assurance in Education, 2006, (14) 3.

J.Rowley, "Motivation and Academics Staff in Higher Education", Journal of Quality Assurance in Education, 4(3), 1996, pp.11-16. crossref(new window)

R. Islam, S.M. Rasad, "Employee performance evaluation by the AHP: A case study", Asia Pacific Management Review, 11(3), 2006, pp.163-176.

F.A. Taylor, A.F. Ketcham, D.Hoffman, "Personnel evaluation with AHP", Management Decision, 36, 1998, pp.679-685. crossref(new window)

C. Moon, J. Lee, C. Jeong, S. Park and S.Lim, "An Implementation Case for the Performance Appraisal and Promotion Ranking", in IEEE International Conference on System, Man and Cybernetics, 2007.

G. Pepiot., N. Cheikhrouhou, J.M. Aurbringer,, G. Glardon, "A fuzzy approach for the valorisation of the competencies", International Conference on Industrial Engineering and Systems Management IESM 2005, May 16-19, Marrakech (Morocco), 2005.

R.C. Jing, C.H. Cheng, L.S.Chen, "A Fuzzy-Based Military Officer Performance Appraisal System", Applied Soft Computing, Vol.7, Issue. 3, 2007, pp.936-945. crossref(new window)

R.D. Andres, J.L.G. Lapresta, M. Jimenez, "A MCDM model for performance appraisal", Methods, Models and Information Technologies for Decision Support Systems Universita del Salento, Lecce, 18-20, pp.77-80, September 2008.

E.P. Paladini, "A Fuzzy Approach to Compare Human Performance in Industrial Plants and Service-Providing Companies", Wseas Transactions on Business and Economics, Issue 11, Vol.6, November 2009, pp.557-569.

G.U. Raju, J. Zhou, R.A. Kiner, "Hierarchical Fuzzy Control", Int. J. Control, 54:5, 1991, pp.1201-1216. crossref(new window)

E.H. Mamdani, S. Assilian, "An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller", International Journal of Man-Machine Studies, 7, 1975, pp.1-13. crossref(new window)

M.L. Lee, H.Y. Chung, F.M. Yu, "Modeling of hierarchical fuzzy systems", Fuzzy Sets and Systems, 138, 2003, pp.343-361. crossref(new window)

J.C.Duan, F.L.Chung, "Multilevel fuzzy relational systems: structure and identification", Soft Computing 6, 2002, pp.71-86. crossref(new window)

R.J.G.B. Campello, W.C. Amaral, Hierarchical Fuzzy Relational Models: Linguistic Interpretation and Universal Approximation, IEEE Transaction on Fuzzy Systems, 14(3), 2006, pp.446-453. crossref(new window)

D. Driankov, H. Hellerdoorn, "Chaining of fuzzy IF-THEN rules in Mamdami controllers", in Proceedings of 1995 IEEE International Conference on Fuzzy Systems FUZZ/IEEE '95, 20th March, 1995.

E.M. Ramirez, R.V. Mayorga, "A cascaded fuzzy inference system for dynamic online portals customization", International Journal of Intelligent Technology, Vol.2, No. 1, 2007, pp.7-20.

J.S.R. Jang, C.T. Sun, E. Mizutani, "Neuro-Fuzzy and Soft Computing: A computational approach to learning and machine intelligence", Matlab Curriculum Series. Edit., Prentice Hall, 1997.