Abstract
In this paper, we propose a Decision Support System based on the MUSA method and the continuous genetic algorithm in order to measure job satisfaction. The objective is to help organizations evaluate and measure their employees’ satisfaction. Our study is composed of two parts. Firstly, we propose to combine continuous genetic algorithm and the MUSA method in order to obtain a robust solution of good performance. The aim of the development of this algorithm is to verify its efficiency regarding the classical MUSA algorithm. Therefore, we compare the result of continuous genetic algorithm with that of the MUSA algorithm. In the second part, we present our Decision Support Systems called “GMUSA System”, it was developed in order to facilitate the applications and the use of the GMUSA tools and overcome the increasing complexity of managerial contexts. Our new system “GMUSA” is applied at the University of Sfax to measure teachers’ job satisfaction.
Highlights
Job satisfaction was studied widely in psychology, it has no single definition but different opinions about it
The data of Decision Support Systems (DSS) are from various sources, such as internal data from the organization, the data generated by different applications, and the external data obtained from the Internet, etc
In order to help any organization evaluate and measure job satisfaction for their employees and that of their customers, we developed a decision support system based on MUSA method and genetic algorithm
Summary
Job satisfaction was studied widely in psychology, it has no single definition but different opinions about it. The skills evaluator adapted to the educational system of Greece is a software tool that implements the proposed multicriteria evaluation method (Razmak and Aouni 2015) Another author represented a DSS for a large scale problem of assigning workers to jobs according to multi criteria in large organizations in order to be able to assign a personnel, according to their capabilities (Constantopoulos 1989). These studies highlight the contribution of the DSS as a support for the utilization of the MCDA methods to deal with some complex decision-making situations For this reason, in the second part of this paper, we develop a decision support system called “GMUSA” in order to facilitate the applications and the use of the GMUSA tools. After a general description of MUSA method, continuous genetic algorithm and Decision support system, we present DSS “GMUSA” in order to measure the university teachers’ job satisfaction
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