Abstract

In today's industrialized world, production is one of the key pillars of the country's progress and plays a decisive role in the economy of nations. Proper management of production systems can dramatically increase system productivity and create a good economic benefit for the manufacturing system. Human resources are one of the most important sources in each production system that play an important role in the productivity and efficiency of the production system. Also, human resources can boost other resources in a production system. So human resource management is very important and necessary. In this paper, a multifunctional fuzzy model for human resource management in production systems is presented. This multi-purpose fuzzy model has been implemented by MATLAB software and the results have been implemented on a real production system.

References

• Shamsaei, N., Yadollahi, A., Bian, L., and Thompson, S.M. 2015. An overview of direct laser deposition for additive manufacturing; part II: mechanical behavior, process parameter optimization and control. Additive Manufacturing. 8, 12-35.Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
• Baykasoglu, A., and Ozsoydan, F.B. 2018. Dynamic scheduling of parallel heat treatment furnaces: a case study at a manufacturing system. Journal of Manufacturing Systems. 46, 152-162. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
• Gahm, C., Denz, F., Dirr, M., and Tuma, A. 2016. Energy efficient scheduling in manufacturing companies: review and research framework. Europe Journal of Operation Research. 248, 744-757.
• Kok, T.G.D. 2018. Modeling short-term manufacturing flexibility by human intervention and its impact on performance. International Journal of Production Research. 56, 447-458. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
• Karatop, B., Kubat, C., and Uygun, O. 2015. Talent Management in Manufacturing System Using Fuzzy Logic Approach. Computers & Industrial Engineering. 86, 127-136.Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
• Boxall, P.F., and Purcell, J. 2003. Strategy and Human Resource Management, Third Edition, Palgrave Macmillan, 2003.
• Watson, T.J. 2010. Critical social science, pragmatism and the realities of HRM. The International Journal of Human Resource Management. 21, 915–31.
• Armstrong, M. 2014. A handbook of human resource management practice. Thirteenth edition. British Library Cataloguing.
• Holbeche, L. 2009. Strategies for Managing and Developing Talent: Strategies for Developing People. Chapter 8. Second Edition. Aligning Human Resources and Business Strategy.
• Michaels, E., Handfield-Jones, H., Beth, A., 2001. The War for Talent. McKinsey & Company.
• Maxwell, G.A., and Maclean, S. 2008. Talent management in hospitality and tourist in Scotland. International Journal of Contemporary Hospitality Management. 2. 820-830.
• Anwar, A., Nasir, Q.A., Khan, N.Z.A., and Sana, A. 2014. Talent management: Strategic Priority of Organizations. International Journal of Innovation and Applied Studies. 9, 1148-1154.
• Huang, L.C., Wu, P., Kuo, R.J., and Huang, H.C. 2001. A neural network modeling on human resource talent selection. International journal of Human Resources Development and Management. 1, 206-219.
• Jantan, H., and Hamdan, A.R. 2010. Human talent prediction in HRM using C4.5 classification algorithm. International journal on computer science and engineering. 2, 2526-2534.
• Aksakal E., Dagdeviren, M., Eraslan, E., and Yuksel, I. 2013. Personnel selection based on talent management. Proceedings of 2nd International Conference on Integrated Information. Budapest, Hungary. 73. 68–72.
• Veluchamy, R., Krishnan, A.R., and Harikumar, K. 2016. Talent management pattern for human resource professionals using fuzzy relational maps (FRM). International Journal of Applied Engineering Research. 11, 564-572.