DECISION MAKING BY SIMULATION- A CASE STUDY

Decision making is a very important area of research. In the era of data, the efforts to support decision making by powerful data processing tools are becoming intense. True and in-time decision is of great importance. For this, decision makers need qualified data and qualified processing. Simulation is the reconstruction of real world scenarios in the virtual environment. It makes possible to analyze the different scenarios that is hard or dangerous to accomplish with a trial and error method. This is a simulation case study in which a hardware maintenance service is the subject. The aim of the study is to illustrate how simulation can be used as a valuable decision support tool for managers and directors by first detecting the bottleneck and then find a solution by applying different scenarios. In this study, a hardware maintenance unit of a hospital, which is suffering for long fixing times, is modelled. Data are collected from the system that the hospital uses. Results show that there is a problem in the firm service unit and it can be overcome by increasing the number of personnel.

Anahtar Kelimeler:

Karar verme, Simülasyon, Modelleme, Performans optimizasyonu

DECISION MAKING BY SIMULATION- A CASE STUDY

Decision making is a very important area of research. In the era of data, the efforts to support decision making by powerful data processing tools are becoming intense. True and in-time decision is of great importance. For this, decision makers need qualified data and qualified processing. Simulation is the reconstruction of real world scenarios in the virtual environment. It makes possible to analyze the different scenarios that is hard or dangerous to accomplish with a trial and error method. This is a simulation case study in which a hardware maintenance service is the subject. The aim of the study is to illustrate how simulation can be used as a valuable decision support tool for managers and directors by first detecting the bottleneck and then find a solution by applying different scenarios. In this study, a hardware maintenance unit of a hospital, which is suffering for long fixing times, is modelled. Data is collected from the system that the hospital uses. Results show that there is a problem in the firm service unit and it can be overcome by increasing the number of personnel.

Keywords:

Decision making, Simulation, Modeling, Performance Optimization,

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Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of applied biomedicine, 11(2), 47-58.
Cheng, M. M., Li, C., Hackett, R. D., & Lee-Chin, M. (2018). Simulation and big data: in search of causality in big data-related managial decision making.
Costin, Y., O'Brien, M. P., & Slattery, D. M. (2018). Using Simulation to Develop Entrepreneurial Skills and Mind-Set: An Exploratory Case Study. International Journal of Teaching and Learning in Higher Education, 30(1), 136-145.
Garalis, A., & Strazdiene, G. (2007). Entrepreneurial skills development via simulation business enterprise. Social Research/ Socialiniai tyrimai, 2(10), 39-48.
Eppich, W., Howard, V., Vozenilek, J., & Curran, I. (2011). Simulation-based team training in healthcare. Simulation in Healthcare, 6(7), S14-S19.Laguna, M., & Marklund, J. (2013). Business process modeling, simulation and design. CRC Press. pp. 253.
Jain, N., & Srivastava, V. (2013). DATA MINING TECHNIQUES: A SURVEY PAPER.IJRET: International Journal of Research in Engineering and Technology,2(11). Retrieved on 11 March 2018 from http://ijret.org/Volumes/V02/I11/IJRET_110211019.pdf
Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. London, Springer.
Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques.San Francisco, CA, itd: Morgan Kaufmann,5.
LeBlanc, V. R., Manser, T., Weinger, M. B., Musson, D., Kutzin, J., & Howard, S. K. (2011). The study of factors affecting human and systems performance in healthcare using simulation. Simulation in Healthcare, 6(7), S24-S29.
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing,66(3), 247-259.
Planas, M. E., García, P. J., Bustelo, M., Cárcamo, C., Ñopo, H. R., Martinez, S., ... & Morrison, A. (2014). Using standardized simulated patients to measure ethnic disparities in family planning services in Peru: Study protocol and pre-trial procedures of a crossover randomized trial. Inter-American Development Bank.
Purniya, R., & Rai, D. (2018). A Comparatively Analysis of Various Manet Based Throughput Enhancement Techniques. International Journal of engineering sciences & Research Technology, 7(2),200-207.
Rohleder, T. R., Lewkonia, P., Bischak, D. P., Duffy, P., & Hendijani, R. (2011). Using simulation modeling to improve patient flow at an outpatient orthopedic clinic. Health Care Management Science, 14(2), 135-145.
Rogers, L. (2011). Developing simulations in multi‐user virtual environments to enhance healthcare education. British Journal of Educational Technology,42(4), 608-615.
Sharma, J., Sharma, M. S., & Pandey, R. (2018). A Complete Review of Concept of Data Mining. International Journal For Technological Research In Engineering, 5(6), 3143-3146.
Slakey, D. P., Simms, E. R., Rennie, K. V., Garstka, M. E., & Korndorffer, J. R. (2014). Using simulation to improve root cause analysis of adverse surgical outcomes. International Journal for Quality in Health Care, 26(2), 144-150.
Wager,K.A., Lee,F.W.,& Glaser,J.P.(2005). Managing Healthcare Information Systems: A Practical Approach for Healthcare Executives. San Francisco:Wiley. pp 92.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. Knowledge and Data Engineering. IEEE Transactions on, 26(1), 97–107.
Yardimci, A. (2009). Soft computing in medicine. Applied Soft Computing, 9(3), 1029-1043.
Zuniga, C., Mujica Mota, M., & Herrera García, A. (2016). Analyzing airport capacity by simulation: a Mexican case study. In A. Ochoa-Zezzatti, J. Sanchez , & M. G. Cedillo-Campos (Eds.), Handbook of research on military, aeronautical, and maritime logistics and operations (pp. 115-150). Hershey, PA: IGI Global.