Cover page and Table of Contents: PDF (size: 563KB)
Full Text (PDF, 563KB), PP.77-86
Views: 0 Downloads: 0
Fuzzy Expert System, Fuzzy Rule, Employee Performance Appraisal
Performance Appraisal of employees plays a very critical role towards the growth of any organization. It has always been a tough task for any industry or organization as there is no unanimous scientific modus operandi for that. Performance Appraisal system is used to assess the capabilities and productiveness of the employees. In assessing employee performance, performance appraisal commonly includes assigning numerical values or linguistic labels to employees performance. However, the employee performance appraisal may include judgments which are based on imprecise data particularly when one employee tries to interpret another employee’s performance. Thus, the values assigned by the appraiser are only approximations and there is inherent vagueness in the evaluation. By fuzzy logic perspective, the performance of the appraisee includes the evaluation of his/her work ability, skills and adaptability which are absolutely fuzzy concepts that needs to be define in fuzzy terms. Hence, fuzzy approach can be used to examine these imprecise and uncertainty information. Consequently, the performance appraisal of employees can be accomplished by fuzzy logic approach and different defuzzification techniques are applied to rank the employees according to their performance, which shows inconsequential deviation in the rankings and hence proves the robustness of the system.
Ashima Aggarwal, Gour Sundar Mitra Thakur, "Design and Implementation of Fuzzy Rule Based Expert System for Employees Performance Appraisal in IT Organizations", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.8, pp.77-86, 2014. DOI:10.5815/ijisa.2014.08.09
S.K. Krishnan, M. Singh (2010).Outcomes of intention to quit of Indian IT Professionals, Wiley, 49(3): 421-437.
M. Armstrong (2006). Performance Management: Key Strategies and Practical Guidelines, Kogan Page Limited, London, UK.
B. Cash (1993). Human Resource Management Handbook, Harvard Business Review, Vol. 3.
K.P. Gallagher, K. M. Kaiser, J.C. Simon (2010). The requisite variety of skills for IT Professionals, Communications of the ACM, 53(6).
C. Moon, J. Lee, C. Jeong, J. Lin, S. Park, S. Lim (2007). An implementation case for the performance appraisal and promotion ranking, IEEE International Conference on Systems, Man and Cybernetics, 2007, ISIC, Page: 661-667.
X. J. Chen, J. P. Zhu, X.Y. Xie, Z.H. Lin (2006). Modeling the performance in Chinese Engineering Managers, IEEE International Engineering Management Conference, Page: 75-77.
P. Jackson (1990). Introduction to expert systems. England: Addison-Wesley.
K. Darlington (2000), The essence of expert system, England: Prentice-Hall.
L. A. Zadeh (1965). Fuzzy sets, Information and Control, Vol 8: 338–353.
A. Abraham (2005). Rule‐Based Expert Systems. Handbook of measuring system design. Wiley.
A. Hajiha, J. J. Jassbi, S. Khanmohammadi (2007). A Fuzzy Expert Decision Support System for Job Assignment, IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007. Page: 1-4.
W. H. Lai, C.T. Tsai (2008). Analyzing Influence Factors of Technology Transfer Using Fuzzy Set Theory, PICMET, Page: 2287-2295.
J. Rezaei, S. Dowlatshahi (2010). A rule based multi-criteria approach to inventory classification, International Journal of Production Research, 48(23): 7107-7126.
M. Fasanghari, G. A. Montazer, (2010). Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation. Expert Systems with Applications, 37(9), 6138–6147.
T. J. Ross (2005). Fuzzy logic with engineering applications. John Wiley & Sons.
Anupama, T. D. Binu M., T. Dulababu (2011). The Need of ‘720 Degree Performance Appraisal’ in the New Economy Companies, International Research of Multidisciplinary Research, 1(4).
A. Neogi, A. C. Mondal, S. K. Mandal (2011). A Cascaded Fuzzy Inference System for University Non-Teaching Staff Performance Appraisal, Journal of Information Processing Systems, 7(4).
O. K. Chaudary, P.G. Khot, K. C. Deshmukh (2012). Soft Computing Model for Academic Performance of Teachers Using Fuzzy Logic, British Journal of Applied Science & Technology, 2(2):213-226.
N. Sapra (2012).Current trends in Performance Appraisal, IJRIM,2(2).
S. Pavani, P.V. S. S. Gangadhar, K. K. Gulhare (2012). Evaluation of Teacher’s Performance using Fuzzy Logic Techniques, International Journal of Computer Trends and Technology, 3(2).