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Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction

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  • Ismahene Aouadni

    (University of Sfax, MODILS, FSEG)

  • Abdelwaheb Rebai

    (University of Sfax, MODILS, FSEG)

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.

Suggested Citation

  • Ismahene Aouadni & Abdelwaheb Rebai, 2017. "Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction," Annals of Operations Research, Springer, vol. 256(1), pages 3-20, September.
    • Handle: RePEc:spr:annopr:v:256:y:2017:i:1:d:10.1007_s10479-016-2154-z
    DOI: 10.1007/s10479-016-2154-z
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    2. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.

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