A Fuzzy Model for Multi-Criteria Evaluation and Ranking of Technicians

Selecting the best technician highly affects the stability and economy of industrial firms. Evaluating technician’s suitability for a job is an important tool for Human Resources Managers (HRMs) to select the better candidates under various evaluation criteria. This paper introduces a fuzzy model in decision competition making for selecting the best technician in any firm or organization. Selected technician must fulfill the machining as well as the human requirement skills. Many of these attributes are of high level of vagueness and imprecision especially in concern with the human psychology. Due to the lack of complete information, uncertainty, and a high level of vagueness and imprecision for technician ranking in any opportunity, a technique to perform selection calculations on imprecise representations of parameters is presented. In this paper, linguistic terms and triangular fuzzy numbers describe decisionmakers’ opinions. A new algorithm is developed based on fuzzy measures to deal with such types of ranking problems. An illustrative example is given to demonstrate the procedure of the proposed methodology. Calculations based on fuzzy weighted average are performed to produce the ratings among selected technician alternatives. A model is conducted for linguistic evaluation for technicians’ skill suitability measures values for different technician alternatives. Two techniques were used in this work for ranking the proposed alternatives; the preference relation using the α-cut, and the fuzzy mean and spread using probability distribution. In general, this method provides accurate selection and can be used easily in industry.