Document Type : Original Article


Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.


Today, the proper and effective performance of employees is one of the keys to the success of organizations. Good performance refers to high efficiency, quality, profitability, and customer orientation. One of the most important duties of human resource managers is to design and establish employee performance evaluation systems. Since qualitative indices have a major share of these indices, judgmental methods are generally used for ranking them. Decision makers assign weights to these indices based on their attitudes and rank the employees. Hence, these methods fail to fully explain the performance of organizations’ employees and are influenced by some degrees and levels of ambiguity. Fuzzy logic methods are highly useful for resolving the ambiguities in these alternatives. In this paper, we propose an employee performance evaluation method with a type-2 fuzzy ranking approach. In our proposed method, a job ID is designed based on optimal models while an employee ranking method is developed and explained using the trapezoidal interval type-2 fuzzy ranking model introduced by Chen et al. 2012. In the end, the proposed method is utilized for the performance evaluation of employees in a real company.


Boral, S., Chaturvedi, S. K., Howard, I., Naikan, V. N. A., & McKee, K. (2021). An integrated interval type-2 fuzzy sets and multiplicative half quadratic programming-based MCDM framework for calculating aggregated risk ranking results of failure modes in FMECA. Process Safety and Environmental Protection, 150, 194-222.
Castillo, O., & Melin, P. (2012). Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review. Information Sciences, 205, 1-19.
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under a fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Systems with Applications, 37(1), 824-833.
Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert systems with applications, 37(4), 2790-2798. Vol.37, pp.2790- 2798.
Chen, S. M., Yang, M. W., Lee, L. W., & Yang, S. W. (2012). Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets. Expert Systems with Applications, 39(5), 5295-5308.
Deveci, M., Simic, V., Karagoz, S., & Antucheviciene, J. (2022). An interval type-2 fuzzy sets-based Delphi approach to evaluate site selection indicators of sustainable vehicle shredding facilities. Applied Soft Computing, 108465.
Falsafi, N., Yousefi Zenouz R. and Mozaffari, M., (2011)."Employees’ performance appraisal with TOPSIS under fuzzy environment", Int. J. Society Systems Science, Vol. 3, pp. 272-290.
Javanmard, M., & Mishmast Nehi, H. (2019). Rankings and operations for interval type-2 fuzzy numbers: a review and some new methods. Journal of Applied Mathematics and Computing, 59(1), 597-630.
Judge, T. A., & Ferris, G. R. (1993). Social context of performance evaluation decisions. Academy of management journal, 36(1), 80-105.
Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE transactions on fuzzy systems, 14(6), 808-821.
Mirzaei Nobari, S., Yousefi, V., Mehrabanfar, E., Jahaniki, A. H., & Khadivi, A. M. (2019). Development of a complementary fuzzy decision support system for employees’ performance evaluation. Economic research-Ekonomska istraživanja, 32(1), 492-509.
Mitchell, H. B. (2006). Ranking type-2 fuzzy numbers. IEEE Transactions on Fuzzy Systems, 14(2), 287-294.
Takáč, Z. (2013). Inclusion and subsethood measure for interval-valued fuzzy sets and for continuous type-2 fuzzy sets. Fuzzy Sets and Systems, 224, 106-120.
Zadeh, L. A. (1965). Information and control. Fuzzy sets, 8(3), 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.
Zhao, M., Qin, S. S., Li, Q. W., Lu, F. Q., & Shen, Z. (2015). The likelihood ranking methods for interval type-2 fuzzy sets considering risk preferences. Mathematical Problems in Engineering, 2015.