KaydedilenlerDerginizi ekleyin

Dergiler / Üniversite Araştırmaları Dergisi / 2026 / Cilt: 7 Sayı: 1

Üniversite Sıralaması İçin Farklı Kriter Ağırlıklandırma ve Çok Kriterli Karar Verme Yöntemlerinin Birlikte Kullanılması: İki Katmanlı Copeland

The use of Different Criteria Weighting and Multi-Criteria Decision Making Methods for University Ranking: Two-Layer Copeland

Mesut Polatgil(Sivas cumhuriyet üniversitesi)0000-0002-7503-2977
Abdulkerim Güler(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0003-4220-918X
Sayfa
60–73
Yayın Tarihi
2024-03-20
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Ensemble Copeland olarak adlandırılan nihai modelin ÇKKV problemlerinde bir ölçüt olarak kullanılabileceği gösterilmiştir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Çok kriterli yöntemler, etkili karar verme için akademi ve endüstri uygulamalarında dikkat çekmektedir.
📊
Temel Bulgular & Veri:Bu sorunu çözmek için farklı ÇKKV yöntemlerinin sonuçlarını birleştiren Copeland ve Borda gibi yöntemler mevcuttur ve yaygın olarak kullanılmaktadır.
💡
Sonuç & Katkı:Ensemble Copeland olarak adlandırılan nihai modelin ÇKKV problemlerinde bir ölçüt olarak kullanılabileceği gösterilmiştir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Çok kriterli karar verme · karar verme · üniversite

Özet

Çok kriterli yöntemler, etkili karar verme için akademi ve endüstri uygulamalarında dikkat çekmektedir. Birçok çok kriterli karar verme (ÇKKV) yöntemi olmasına rağmen, bu yöntemlerin hiçbiri mükemmel değildir ve karar problemine göre seçilmelidir. Karar vericiye uygun olan doğru çözümü bulmak için gerekli karar destek yöntemini seçmek önemli bir sorun haline gelmektedir. Bu sorunu çözmek için farklı ÇKKV yöntemlerinin sonuçlarını birleştiren Copeland ve Borda gibi yöntemler mevcuttur ve yaygın olarak kullanılmaktadır. Bu çalışmada, farklı ÇKKV yöntemlerinin sonuçlarının farklı kriter ağırlıklandırma yöntemleri ile iki kez birleştirilmesine dayanan yeni bir hibrit model önerilmiştir. Önerilen model, Türkiye'deki 20 vakıf üniversitesinin öğrenci memnuniyeti verileri üzerinde test edilmiştir. Ensemble Copeland olarak adlandırılan nihai modelin ÇKKV problemlerinde bir ölçüt olarak kullanılabileceği gösterilmiştir.

Abstract

Multi-criteria methods have attracted attention in academia and industry applications for effective decision making. Although there are many multi-criteria decision making (MCDM) methods, none of these methods are perfect and should be chosen according to the decision problem. Choosing the necessary decision support method to find the right solution that is suitable for the decision maker becomes an important problem. To solve this problem, methods such as Copeland and Borda combining the results of different MCDM methods are available and widely used. In this study, a new hybrid model is proposed based on twice combining the results of different MCDM methods with different criterion weighting methods. The proposed model has been tested on student satisfaction data of 20 foundation universities in Turkey. It has been shown that the final model named Ensemble Copeland can be used as a benchmark in MCDM problems

🔖

Bu Makaleye Atıf Yap

Polatgil, M., Güler, A. (2024). Üniversite Sıralaması İçin Farklı Kriter Ağırlıklandırma ve Çok Kriterli Karar Verme Yöntemlerinin Birlikte Kullanılması: İki Katmanlı Copeland. Üniversite Araştırmaları Dergisi, Cilt: 7 Sayı: 1, 60–73. https://doi.org/10.32329/uad.1398302

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (36)

  1. Adil, M., Nunes, M. B., & Peng, G. C. (2014). Identifying operational requirements to select suitable decision models for a public sector e-procurement decision support system. Journal of Information Systems and Technology Management, 11(2), 211–228. https://doi.org/10.4301/10.4301
  2. Al-Shemmeri, T., Al-Kloub, B., & Pearman, A. (1997). Model choice in multicriteria decision aid. European Journal of Operational Research, 97(3), 550–560. https://doi.org/10.1016/S0377-2217(96)00277-9
  3. Animah, I., & Shafiee, M. (2021). Maintenance strategy selection for critical shipboard machinery systems using a hybrid AHP-PROMETHEE and cost benefit analysis: a case study. Journal of Marine Engineering \& Technology, 20(5), 312–323.
  4. Azimi, S. A. Z., & Makui, A. (2017). Prioritizing the Components Affecting Patient’s Satisfaction with Healthcare Services using Multiple Attribute Decision Making Technique. Industrial Engineering and Management Systems, 16(4), 479–485. https://doi.org/10.7232/iems.2017.16.4.479
  5. Baczkiewicz, A., Kizielewicz, B., Shekhovtsov, A., Watróbski, J., & Sałabun, W. (2021). Methodical aspects of mcdm based e-commerce recommender system. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2192–2229. https://doi.org/10.3390/JTAER16060122
  6. Basílio, M. P., Pereira, V., Costa, H. G., Santos, M., & Ghosh, A. (2022). A Systematic Review of the Applications of Multi-Criteria Decision Aid Methods (1977–2022). Electronics 2022, Vol. 11, Page 1720, 11(11), 1720. https://doi.org/10.3390/ELECTRONICS11111720
  7. Beheshtinia, M. A., & Omidi, S. (2017). A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386–1407. https://doi.org/10.1108/K-03-2017-0105
  8. Biswas, S., Bandyopadhyay, G., & Mukhopadhyaya, J. N. (2022). A multi-criteria based analytic framework for exploring the impact of Covid-19 on firm performance in emerging market. Decision Analytics Journal, 5. https://doi.org/10.1016/J.DAJOUR.2022.100143
  9. Biswas, S., Bandyopadhyay, G., Pamucar, D., & Joshi, N. (2022). A Multi-criteria Based Stock Selection Framework In Emerging Market. Operational Research in Engineering Sciences: Theory and Applications, 5(3), 153–193. https://doi.org/10.31181/ORESTA161122121B
  10. Chang, Y. H., Yeh, C. H., & Chang, Y. W. (2013). A new method selection approach for fuzzy group multicriteria decision making. Applied Soft Computing, 13(4), 2179–2187. https://doi.org/10.1016/J.ASOC.2012.12.009
  11. Chisale, S. W., Eliya, S., & Taulo, J. (2023). Optimization and design of hybrid power system using HOMER pro and integrated CRITIC-PROMETHEE II approaches. Green Technologies and Sustainability, 1, 100005.
  12. Cinelli, M., Kadziński, M., Gonzalez, M., & Słowiński, R. (2020). How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega, 96, 102261. https://doi.org/10.1016/J.OMEGA.2020.102261
  13. Demir, G., & Arslan, R. (2022). Sensitivity Analysis in Multi-Criterion Decision-Making Problems. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(3), 1025–1056. https://doi.org/10.26745/ahbvuibfd.1103531
  14. Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 143. https://doi.org/10.1016/j.rser.2021.110916
  15. Gershon, M., & Duckstein, L. (1983). An Algorithm for Choosing of a Multiobjective Technique BT - Essays and Surveys on Multiple Criteria Decision Making (P. Hansen (ed.); pp. 53–62). Springer Berlin Heidelberg.
  16. Greco, S., Figueira, J., & Ehrgott, M. (2005). Multiple criteria decision analysis. Springer.
  17. Guitouni, A., & Martel, J. M. (1998). Tentative guidelines to help choosing an appropriate MCDA method. European Journal of Operational Research, 109(2), 501–521. https://doi.org/10.1016/S0377-2217(98)00073-3
  18. Hajkowicz, S., & Higgins, A. (2006). A comparison of multiple criteria analysis techniques for water resource management. https://doi.org/10.1016/j.ejor.2006.10.045
  19. Hu, Z. J., & Lin, J. (2022). An integrated multicriteria group decision making methodology for property concealment risk assessment under Z-number environment. EXPERT SYSTEMS WITH APPLICATIONS, 205. https://doi.org/10.1016/j.eswa.2022.117369
  20. Hwang, C.-L., & Yoon, K. (1981). Methods for Multiple Attribute Decision Making BT - Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey (C.-L. Hwang & K. Yoon (eds.); pp. 58–191). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9_3
  21. Ishizaka, A., & Resce, G. (2021). Best-Worst PROMETHEE method for evaluating school performance in the OECD’s PISA project. Socio-Economic Planning Sciences, 73, 100799.
  22. Khan, S., & Purohit, L. (2022). An Integrated Methodology of Ranking Based on PROMETHEE-CRITIC and TOPSIS-CRITIC In Web Service Domain. 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 335–340.
  23. Kilic, H. S., Zaim, S., & Delen, D. (2015). Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Systems with Applications, 42(5), 2343–2352.
  24. Moffett, A., & Sarkar, S. (2006). Incorporating multiple criteria into the design of conservation area networks: a minireview with recommendations. Diversity and Distributions, 12(2), 125–137. https://doi.org/10.1111/J.1366-9516.2005.00202.X
  25. Moghaddam, N. B., Nasiri, M., & Mousavi, S. M. (2011). An appropriate multiple criteria decision making method for solving electricity planning problems, addressing sustainability issue. International Journal of Environmental Science & Technology, 8(3), 605–620. https://doi.org/10.1007/BF03326246
  26. Mohebali, S., Maghsoudy, S., & Doulati Ardejani, F. (2020). Coupled multi-criteria decision-making method: A new approach for environmental impact assessment of industrial companies. Environmental Progress and Sustainable Energy, 39(6). https://doi.org/10.1002/EP.13523
  27. Nasiri, M., Moghaddam, H. K., & Hamidi, M. (2021). Development of Multi-Criteria Decision Making Methods for Reduction of Seawater Intrusion in Coastal Aquifers Using SEAWAT Code. Journal of Contaminant Hydrology, 242. https://doi.org/10.1016/J.JCONHYD.2021.103848
  28. Roy, B. (1996). Multicriteria Methodology for Decision Aiding. 12. https://doi.org/10.1007/978-1-4757-2500-1 Şahin, M. (2021). Location selection by multi-criteria decision-making methods based on objective and subjective weightings. Knowledge and Information Systems, 63(8), 1991–2021. https://doi.org/10.1007/S10115-021-01588-Y
  29. Śak, J. (2005). The Comparison Of Multiobjective Ranking Methods Applied To Solve The Mass Transit Systems’ Decision Problems. In Proceedings of the 10th Jubilee Meeting of the EURO Working Group on Transportation, 13–16.
  30. Sałabun, W., Watróbski, J., & Shekhovtsov, A. (2020). Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods. Symmetry 2020, Vol. 12, Page 1549, 12(9), 1549. https://doi.org/10.3390/SYM12091549
  31. Sayadinia, S., & Beheshtinia, M. A. (2021). Proposing a new hybrid multi-criteria decision-making approach for road maintenance prioritization. International Journal of Quality & Reliability Management, 38(8), 1661–1679. https://doi.org/10.1108/IJQRM-01-2020-0020
  32. Setiawan, H., Istiyanto, J. E., Wardoyo, R., & Santoso, P. (2016). The Group Decision Support System to Evaluate the ICT Project Performance Using the Hybrid Method of AHP, TOPSIS and Copeland Score. International Journal of Advanced Computer Science and Applications, 7(4), 334–341.
  33. Singh, A., Gupta, A., & Mehra, A. (2021). Best criteria selection based PROMETHEE II method. Opsearch, 58, 160–180.
  34. Tajik, M., Makui, A., & Tosarkani, B. M. (2023). Sustainable cathode material selection in lithium-ion batteries using a novel hybrid multi-criteria decision-making. Journal of Energy Storage, 66, 107089. https://doi.org/10.1016/J.EST.2023.107089
  35. Torkzad, A., & Beheshtinia, M. A. (2019). Evaluating and prioritizing hospital service quality. International Journal of Health Care Quality Assurance, 32(2), 332–346. https://doi.org/10.1108/IJHCQA-03-2018-0082
  36. Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507–529. https://doi.org/10.1016/S0377-2217(97)00147-1