Y. Zare Mehrjerdi - Department Industrial Engineering, Yazd University, Yazd, Iran,
T. Aliheidary - Department of Economics, Science and Research Branch, Islamic Azad University, Yazd, Iran

This study presents an integrated intelligent algorithm to valuate the level of job satisfaction(JS) in services. Job Satisfaction plays an important role as a competitive advantage in organizations especially in helth industry. Recruitment and retention of human resources are persistent problems associated with this field. Most of the researchs have focused on job satisfaction factors and few of researches have noticed about its effects on productivity. However, little researchs have focused on the factors and effects of job satisfaction simultanosly by system dynamics approaches.In this paper, firstly, analyses the literature relating to system dynamics and job satisfaction in services specially at a hospital clinic and reports the related factors of employee job satisfaction and its effects on productivity. The conflicts and similarities of the researches are discussed and argued. Then a novel procedure for job satisfaction evaluation using (Artificial Neural Networks)ANNs and system dynamics is presented. The proposed procedure is implemented for a large hospital in Iran. The most influencial factors on job satisfaction are chosen by using ANN and three differents dynamics scenarios are built based on ANN's result. The modelling effort has focused on evaluating the job satisfaction level in terms of key factors which obtain from ANN result such as Pay, Work and Co-Workers at all three scenarios. The study concludes with the analysis of the obtained results. The results show that this model is significantly usfule for job satisfaction evaluation and provide policy makers with an appropriate tool to make more accurate decision on kind of reward. This is because the proposed approach is capable of handling non-linearity, complexity as well as uncertainty that may exist in actual data and situation.

Job Satisfaction, System dynamics, Artificial Neural Network (ANN), Healthcar field,