ENTROPY-ARAS and ENTROPY-MOOSRA Based Assessment of Quality of Life in EU Countries
Öz
Quality of life is an emerging term of modern times as a result of ever increasing civilization of societies. Existing studies suggest that thanks to developments in technology and increase in income levels, welfare is no longer the only indicator of quality of life but environmental, social, and political factors have also significant impact on it. In this study, ENTROPY, ARAS and MOOSRA multi criteria decision making methods have been used to assess quality of life in EU countries. Different life quality criteria have been used in the literature. The criteria that was used in this study to assess the quality of life in EU consist of the factors, provided in Numbeo website in 2016 such as purchasing power, safety, health, climate, cost of life, cost of property, time spent on traffic and pollution. Entropy method has been used to calculate the criteria weights. Pollution has been found as the most important criteria of life quality in EU countries. ARAS and MOOSRA methods have been used to compare the countries. Finding of both methods highlight that Finland is the best country in terms of life quality.
Anahtar Kelimeler
Qualiy of Life,Multi Criteria Decision Making,ENTROPY,ARAS,MOOSRA
Kaynakça
- Adalı, E. & Işık, A. (2016). Air conditioner selection problem with copras and aras methods. Manas Journal of Social Studies, 5 (2), 124-138.
- Adalı, E.A. & Işık, A.T. (2016). the multi-objective decision making methods based on multımoora and moosra for the laptop selection problem. Journal of Industrial Engineering International-Springer, 1-9.
- Alp, İ., Öztel, A. & Köse, M.S. (2015). Entropi tabanlı maut yöntemi ile kurumsal sürdürülebilirlik performansı ölçümü: bir vaka çalışması. Ekonomik ve Sosyal Araştırmalar Dergisi, 11 (2), 65-82.
- Bakshi, T. & Sarkar, B. (2011). MCA based performance evaluation of project selection. International Journal of Software Engineering & Applications, 2 (2), 14-22.
- Balezentiene, L., Streimikiene, D. & Balezentis, T. (2013). Fuzzy decision support methodology for sustainable energy crop selection. Renewable and Sustainable Energy Reviews, 17, 83-93.
- Bilien, U. & Tassinopoulos, A. (2001). Forecasting regional employment with the entropy method. European Congress of the Regional Science Association, 35 (2), 113-124.
- Chatterjee, P. & Chakraborty, S. (2012). Material selection using preferential ranking methods. Materials and Design, 35, 384-393.
- Chen, J., Zhang, Y., Chen, Z. & Nie, Z. (2015). Improving assessment of groundwater sustainability with analytic hierarchy process and information entropy method: a case study of the hohhot plain, china. Environment Earth Science, 73 (5), 2353-2363.
- Chen, W., Feng, D. & Chu, X. (2015). Study of poverty alleviation effects for chinese fourteen contiguous destitute areas based on entropy method. International Journal of Economics and Finance, 7 (4), 89-98.
- Çınar, Y. (2004). Çok nitelikli karar verme ve bankaların mali performanslarının değerlendirilmesi örneği. Yayınlanmamış Yüksek Lisans Tezi, Ankara Üniversitesi Sosyal Bilimler Enstitüsü İşletme Anabilim Dalı, Ankara, Turkey.
