A Cascaded Fuzzy Inference System for University Non-Teaching Staff Performance Appraisal

Abstract

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.

An Automated Implementation of Academic Staff Performance Evaluation System based on Rough Sets Theory

“…Ojokoh, Akinsulire & Isinkaye 2019, Vol 23, Research Article Academic Staff Performance Evaluation System based on Rough Sets Theory Neogi et al (2011) proposed a system that was based on the articulation of cascading fuzzy inference system and centre of gravity (COG) defuzzification method. The Fuzzy Inference System (FIS) module contains five FISs sub-modules in cascade named fuzzy communication block.…”

“…The guidelines of this assessment are always aligned with the mission and goals of the institution in order to contribute towards its success. Academic staff members' evaluation has potentially dual impact on the performance of the institution and on individual staff members' activity orientation and performance, given the effect on their career and progression in life (Neogi et al 2011, Ranjan et al 2015. Human resource evaluation is an optimization problem and a multiple criteria decision making process.…”

The Development of a Performance Appraisal System Using Decision Tree Analysis and Fuzzy Logic

“…Fuzzy logic maybe used in the evaluation approach because the performance of the appraisal involves the measurement of ability, competence and skills, which are fuzzy concepts that may be captured in fuzzy terms. As a result, fuzzy logic approach can be implemented to manage the uncertainty of information and ensure quality involved in staff performance evaluation [2]. Also, researchers have proven the success of fuzzy set theory in solving multiple criteria problems [3].…”

Modeling Academic Performance Evaluation using Fuzzy C-Means Clustering Techniques

“…have defined fuzzy membership functions for fuzzy rules [6]; Law C.K. has used fuzzy numbers [7], and for more information on this issue consult: Chen and Lee [8], Chiu-Keung Law [7], Wang and Chen [9], Stathacopoulou [10], Guh [11], Gokmen [12], Hameed 13], Sirigiri Pavani [26], Neogi [30], Yadav [31], Gupta [18], Krzysztof [20], Mamatha [33], Chaudhari [29], Daud [32], Baylari and Montazer [14], Posey and Hawkes [15], Stathacopoulou [16], Bhatt and Bhatt [17], and Zhou and Ma [19]. The research works cited in the preceding paragraph indicates that the fuzzy logic, neural network and fuzzy neural network have already been employed in student modeling systems but almost nothing or very little has been mentioned about automatic generation of fuzzy membership function.…”