Measuring job satifaction based on fuzzy multi-criteria satisfaction analysis (FMUSA) method and continuous genetic algorithms

Abstract

This paper proposes an extension of the well-known MUSA method to fuzzy environment. The Fuzzy MUSA method gives much flexibility to decision makers because the majority of real-life decision problems were characterized by the uncertainty that hinders them from assigning exact evaluations to options The objective of the Fuzzy MUSA method is to make the method capable of accepting and processing fuzzy scores as input and producing a satisfaction function with fuzzy coefficients, i.e. a fuzzy partial satisfaction function and fuzzy global satisfaction. All the parameters used in the classic MUSA method have their counterpart in the proposed method. Then we combine Fuzzy MUSA method with the continuous genetic algorithm in order to obtain a robust solution of good performance. Finally we apply our approach at University of Sfax in order to measure teachers' job satisfaction because the university teachers' job satisfaction has a constructive role in raising the level of teaching and research, enhancing academic competitiveness, attracting and retaining talented people.