Document Type : Research Paper
Authors
1 Assistant Professor of Sport Management, University of Isfahan, Isfahan, Iran
2 Assistant Professor of Industrial Management, University of Isfahan, Isfahan, Iran
3 Ph.D. Student in Industrial Management, University of Isfahan, Isfahan, Iran
4 M.A. Student of Industrial Management, University of Isfahan, Isfahan, Iran
Abstract
One of the most important events in the field of sport sciences is the practical physical education test. The significance of this test is due to the sensitivity of choosing the right people to enter this field and leaving expert and competent people in the end. Therefore, in this regard implementing an optimized test that has the maximum efficiency and effectiveness is very important and necessary. In this regard, the purpose of this research was to optimize the practical physical education test process using simulation and multi-criteria decision-making methods. The test was designed using the ARENA simulation model in order to select the appropriate method of test implementation, in addition, to determine the optimal number of necessary resources. For this purpose, eight different scenarios were first designed and then modeling in the ARENA software. In the design of scenarios, the process of entering volunteers, the process of setup the physical and medical status assessment station and the number of stations in the section of physical fitness was considered. Multi-criteria decision-making methods were used to select the best scenario, taking into account the appropriate criteria (volunteer waiting time, execution time process, average utilization of resources, and total cost of resources). In this method, first, determine the weight of the criteria with the CRITIC method and then the best scenario was selected using the PROMETHEE method. The results showed that scenario 6 is the best scenario and scenario 3 is the worst scenario. The optimization of the physical education test process will help the organizers to resolve some of the problems in this field and thus improve the results of the test, especially in the implementation section.
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2. Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodology and applications. European Journal of Operations Research, 200(1), 198-215. (Persian).
3. Diakoulaki, D., Mavrotas, G., Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers and Operations Research, 22(7), 763-70.
4. Esmaelian, M., & Mohammadi, S. (2016). Multi-purpose decision making techniques (with software implementation). Isfahan: University of Isfahan. (Persian).
5. Farahani, A. (2012). Compilation of the national norm of male volunteer’s motor readiness of physical education entrance tests of Payam Noor University. Applied Research in Sport Management, 3, 51-6. (Persian).
6. Ganjali, M., Rasoli Moghaddam, M., & Maghsudi, E. (2013). Simulation of supply chain using ARENA software. Proceedings of Second National Conference on Industrial Engineering and Systems (1-10). Najaf Abad: Islamic Azad University, Najaf Abad Branch. (Persian).
7. Hadavi, F. (2000). Experimental design of volunteer’s practical test of entrance to physical education at throughout universities of country. Sport Sciences Research Center. Available at: http://ensani.ir. (Persian).
8. Kashif, M., & Bonyan, A. (2009). Study and review the practical entrance test of physical education field. Research in Sport Sciences, 22, 13-25. (Persian).
9. Moradi, H., & Razavi, M. (2016). The simulation model for paraclinical service in Hafez hospital in Shiraz, Iran, and evaluation of scenarios to reduce waiting time. Health Information Management Journal, 13(1), 11-8. (Persian).
10. Rabiei, H., & Zolaktaf, V. (2001). Objectivity, validity and reliability of the practical test 1380 for physical education student selection. Olympics Quarterly, 11(1, 2), 13-23.
11. Villamizar, J. R., Coelli, F. C., Pereira, W., & Almeida, R. (2011). Discrete-event computer simulation methods in the optimization of a physiotherapy clinic. Physiotherapy, 97(1), 71-7.
12. Zare Mehrjardi, Y., Hoboubati, M., & Safaee Nik, F. (2011). Improvement of waiting time for patients referring to emergency room using discrete event simulation. Journal of Shahid Sadoughi University of Medical Sciences, 19(3), 302-12. (Persian).
13. Zhen, L., Wang, K., & Chang, D. (2014). A simulation optimization framework for ambulance deployment and relocation problems. Computers & Industrial Engineering, 13(2), 89-103.
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