Analysis of Factors Affecting Density of Vaccine Centers by Fuzzy ELECTRE I Method

Abstract

Nowadays, the increase in vaccinations due to the epidemic increases the density of health centers and shopping centers, which is another place where vaccinations are made, from time to time. The choice of vaccination sites varies from person to person, and this may increase the density of the vaccination sequence. The criteria that cause the increase in density were created by consulting the health personnel, the people who will be vaccinated and those who have had the vaccine, taking into account the conditions and situations that people care about. Alternatively, three different vaccine centers were selected. These centers are designated as hospitals, health centers and shopping centers. Care has been taken to ensure that the designated vaccination centers are places where daily vaccinations are performed. In order to determine the criteria, the opinions of the people were taken by applying a questionnaire. Thus, in this study, which was conducted for the first time, it was investigated which places were more dense with the determined nine criteria and the parameters affecting this density were tried to be analyzed with the Fuzzy ELECTRE I method. Knowing the reasons for these densities and revealing which institutions people frequently prefer for vaccination can prevent problems.

Keywords

• Fuzzy ELECTRE I
• Decision Making Methods
• Fuzzy Decision Making

References

1. Abdel-Basset, M., Gunasekaran, M., Mohamed M, & Chilamkurti N, (2019). A framework for risk assessment, management and evaluation: Economic tool for quantifying risks in supply chain. Future Generation Computer Systems, 90: 489-502.
2. Akkaya G, Turanoğlu, B. & Öztaş, S., (2015) An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing, Expert Systems with Applications, 42(24): 9565-9573.
3. Akram, M., Garg, H. & Zahid K. (2020). Extensions of ELECTRE-I and TOPSIS methods for group decision-making under complex Pythagorean fuzzy environment. Iranian Journal of Fuzzy Systems, 17(5):147-164.
4. Alizadeh, S., Rad, MMS., & Bazzazi, A.A. (2016). Alunite processing method selection using the AHP and TOPSIS approaches under fuzzy environment. International Journal of Mining Science and Technology, 26(6): 1017-1023,
5. Beşikçi, E.B., Kececi, T., Arslan., O, & Turan O, (2016). An application of fuzzy-AHP to ship operational energy efficiency measures. Ocean Engineering, 121: 392-40.
6. Buckley, J.J., (1985). Fuzzy hierarchical analysis, Fuzzy sets and systems, 17(3):233-247. Calabrese, A., Costa, R., Levialdi, N., Menichini, T. (2018). Integrating sustainability into strategic decision-making: A fuzzy AHP method for the selection of relevant sustainability issues, Technological Forecasting and Social Change.
7. Capocelli, R.M., De Luca, A. (1973). Fuzzy sets and decision theory, Information and control, 23(5): 446-473.
8. Dožić, S. (2018). Lutovac, T. ve Kalić, M. Fuzzy AHP approach to passenger aircraft type selection. Journal of Air Transport Management, 68: 165-175.

9. Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of environmental management, 226: 201-216.
10. Güler, M.E. (2012). Prioritization of Revenue Management Factors: A Synthetic Extent Analysis Approach/Getiri Yönetimi Faktörlerinin Önceliklendirilmesi: Sentetik Kapsam Analizi Yaklasimi, Ege Akademik Bakis, 12(2): 161.
11. Gülsün, B., Erdoğmuş, K,N. (2021). Bankacılık Sektöründe Bulanık Analitik Hiyerarşi Prosesi ve Bulanık TOPSIS Yöntemleri ile Finansal Performans Değerlendirmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1): 1-15.
12. Heo E, Kim J, Boo K-J, (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews, 14(8): 2214-2220.
13. Junior FRL, Osiro L, ve Carpinetti LCR, (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection, Applied Soft Computing, 21: 194-209.
14. Li P, Zhang L, Dai L, Zou Y, Li X, (2018). An assessment method of operator’s situation awareness reliability based on fuzzy logic-AHP. Safety Science.
15. Li S, Wei & Z, (2018). A hybrid approach based on the analytic hierarchy process and 2-tuple hybrid ordered weighted averaging for location selection of distribution centers. PloS one, 13(11):e0206966.
16. Ligus M, & Peternek, P. (2018). Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method, Energy Procedia, 153: 101-106.
17. Ly PTM, Lai WH, Hsu CW, Shih FY, (2018). Fuzzy AHP analysis of Internet of Things (IoT) in enterprises. Technological Forecasting and Social Change, 136: 1-13.
18. Mandic K, Delibasic B, Knezevic S, Benkovic S (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43: 30-37.
19. A, Rahman T, Jabbour CJC, Ali SM, Kabir G ( 2018).Prioritization of drivers of corporate social responsibility in the footwear industry in an emerging economy: A fuzzy AHP approach. Journal of Cleaner Production, 201:369-381.
20. Özçakar N, Demir HH, (2011). Bulanık TOPSIS Yöntemi ile Tedarikçi Seçimi. İstanbul Üniversitesi İşletme İktisadı Enstitüsü Dergisi, 22(69): 28-33.
21. Pandey A, Kumar A, (2017). Commentary on “Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach. Applied Soft Computing, 51: 351-352.
22. Sharma YK, Yadav AK, Mangla SK Patil PP, (2018). Ranking the Success Factors to Improve Safety and Security in Sustainable Food Supply Chain Management Using Fuzzy AHP, Materials Today: Proceedings, 5(5): 12187-12196.
23. Sirisawat P, Kiatcharoenpol T, (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117:303-318.
24. Taylan O, Bafail AO, Abdulaal RM, Kabli MR, 2014. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17: 105-116.
25. Wu MC, Chen TY. (2011). The ELECTRE Multicriteria Analysis Approach Based on Atanassov’s Intuitionistic Fuzzy Sets. Expert Systems withApplications. 38(10):12318-12327.
26. Zadeh LA, 1968. Probability measures of fuzzy events, Journal of mathematical analysis and applications, 23(2): 421-427.
27. Zile M, 2015. İş Güvenliği Risk Değerlendirme Analiz Modellemesi ve Yazılımının Bulanık Mantıkla Oluşturulması. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 30(2): 267-274.