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COCOSO YÖNTEMİ İÇİN NORMALİZASYON PROSEDÜRLERİ: FARKLI SENARYOLAR ALTINDA KARŞILAŞTIRMALI BİR ANALİZ

Yıl 2021, Cilt: 22 Sayı: 2, 217 - 234, 04.02.2022
https://doi.org/10.24889/ifede.974252

Öz

Karar matrisinin oluşturulmasının ardından ÇKKV yöntemlerinde ilk adım normalizasyon işlemidir. Normalizasyon ÇKKV yöntemlerinde en önemli süreçlerden biridir ve ÇKKV sıralama sonuçları üzerinde etkilidir. Bu nedenle karar problemlerinde uygun normalizasyon tekniğinin seçilmesi çok önemlidir. Bu çalışma, normalizasyon tekniklerinin farklı senaryolar altında CoCoSo yöntemi sonuçları üzerindeki etkisini ortaya koymayı ve uygun bir normalizasyon tekniğini seçmeyi amaçlamaktadır. Çalışma sonunda, N3, N4 ve N6 normalizasyon tekniklerinin CoCoSo yönteminin kendi algoritmasında bulunan max min normalizasyon tekniğine alternatif olarak kullanılabileceği tespit edilmiştir. Ayrıca N1 ve N2 normalizasyon tekniklerinin CoCoSo yöntemi için uygun olmadığı tespit edilmiştir. Bu çalışmada farklı normalizasyon tekniklerinin CoCoSo yöntemine uygunluğu ilk kez test edilmiştir.

Kaynakça

  • Abdel-Basset, M., Ding, W., Mohamed, R., & Metawa, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing ındustries. Risk Management, 22(3), 192-218.
  • Asgharpour, M. J. (1998). Multiple criteria decision making. Tehran: Tehran University Press.
  • Brans, J. P., & Vincke, P. (1985). A preference ranking organisation method (the promethee method for multiple criteria decision-making). Management Science, 31(6), 647-656.
  • Brauers, W.K., & Zavadskas, E.K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 443–468.
  • Brauers, W.K.M., & Zavadskas, E.K. (2009). Robustness of the multi-objective MOORA method with a test for the facilities sector. Technological and Economic Development of Economy: Baltic J on Sustainability, 15: 352-375.
  • Celen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: With an application to turkish deposit banking market. Informatica, 25(2), 185-208.
  • Chakraborty, S., & Yeh, C. H. (2007). A simulation based comparative study of normalization procedures in multiattribute decision making. 6th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, 102-109.
  • Chakraborty, S., & Yeh, C.H. (2009). A simulation comparison of normalization procedures for TOPSIS. Computing Industrial Engineering, 5(9):1815–1820.
  • Chakraborty, S., & Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25 (1): 1–20.
  • Chatterjee, P., & Chakraborty, S. (2014). Investigating the effect of normalization norms in flexible manufacturing sytem selection using multi-criteria decision-making methods. Journal of Engineering Science & Technology Review, 7(3): 141-150.
  • Delft, A. D., & Nijkamp, P. (1977). Multi-Criteria analysis and regional decision-making. Springer Science & Business Media, Berlin, Germany.
  • Durucasu, H., Aytekin, A., Saraç, B., & Orakçı, E (2017). Current application fields of ELECTRE and PROMETHEE: A literature review. Alphanumeric Journal, 5(2), 229-270.
  • Ersoy, N. (2020). Selecting the best normalization technique for ROV method: Towards a real life application. Gazi University Journal of Science. 34(2):592-609.
  • Farag, M. M. (1997). Materials selection for engineering design. USA: Prentice Hall.
  • Ghorabaee, M. K., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision making. Economic Computation and Economic Cybernetics Studies and Research, 3(50), 25-44.
  • Huang, W. C., & Chen, C. H. (2005). Using the ELECTRE II method to apply and analyze the differentation theory. Proceedings of the Eastern Asia Society for Transportation Studies, 2237-2249.
  • Jahan, A., & Edwards, K. L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 335-342.
  • Jahan, A., Bahraminasab, M., & Edwards, K. L. (2012). A target-based normalization technique for materials selection. Materials & Design, 35, 647-654. Jee, D. H., & Kang, K. J. (2000). A method for optimal material selection aided with decision making theory. Materials & Design, 21(3): 199-206.
  • Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). Simultaneous evaluation of criteria and alternatives (SECA) for multi-criteria decision-making. Informatica, 29(2), 265-280.
  • Kosareva, N., Krylovas, A., & Zavadskas, E. K. (2018). Statistical analysis of MCDM data normalization methods using monte carlo approach. The Case of Ternary Estimates Matrix. Economic Computation and Economic Cybernetics Studies and Research, 52, 159-175.
  • Krishankumar, R., Premaladha, J., Ravichandran, K. S., Sekar, K. R., Manikandan, R., & Gao, X. Z. (2020). A novel extension to VIKOR method under intuitionistic fuzzy context for solving personnel selection problem. Soft Computing, 24(2), 1063-1081.
  • Lai, Y.J., & Hwang, C.L. (1994). Fuzzy multiple objective decision making: methods and applications. Berlin: Springer-Verlag.
  • Lakshmi, T. M., & Venkatesan, V. P. (2014). A comparison of various normalization in techniques for order performance by similarity to ideal solution (TOPSIS). International Journal of Computing Algorithm, 3, 882-888.
  • Madić, M., & Radovanović, M. (2015). Ranking of some most commonly used non-traditional machining processes using ROV and CRITIC methods. UPB Scientific bulletin, Series D: Mechanical Engineering, 77(2), 193-204.
  • Mahmoudi, A., Deng, X., Javed, S. A., & Yuan, J. (2020). Large-scale multiple criteria decision-making with missing values: Project selection through TOPSIS-OPA. Journal of Ambient Intelligence and Humanized Computing, 1-22.
  • Mathew, M., Sahu, S., & Upadhyay, A. K. (2017). Effect of normalization techniques in robot selection using weighted aggregated sum product assessment. International journal of innovative research and advanced studies, 4(2):59-63.
  • Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A. (2005). The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural and Multidisciplinary Optimization, 29(4), 312-318.
  • Mufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438.
  • Opricovic, S., & Tzeng, G.H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur J Oper Res,156:445–55.
  • Özdağoğlu, A. (2013). The effects of different normalization methods to decision making process in TOPSIS. Ege Academic Review, 13(2), 245-258.
  • Pavlicic, D. (2001). Normalization affects the results of MADM methods. Yugoslav Journal of Operations Research, 11(2): 251–265.
  • Peldschus, F., Vaigauskas, E., & Zavadskas, E. K. (1983). Technologische entscheidungen bei der berücksichtigung mehrerer ziehle. Bauplanung Bautechnik, 37(4): 173-175.
  • Saaty, T. L. (1980).The analytic hierarchy process. New York: McGraw-Hill.
  • Shanian, A., & Savadogo, O. (2006). TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of Power Sources, 159(2): 1095-1104.
  • Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An Extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8): 801-813.
  • Stanujkic, D., Dordevic, B., & Dordevic, M. (2013). Comparative analysis of some prominent MCDM methods: a case of ranking serbian banks. Serbian Journal of Management, 8(2): 213-241.
  • Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare ındustries using a new MCDM method: measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140 (106231), 1-15.
  • Tabucanon, M.T. (1988). Multiple criteria decision making in industry. Elsevier, Amsterdam, The Netherlands.
  • Tadić, S., Krstić, M., Roso, V., & Brnjac, N. (2020). Dry port terminal location selection by applying the hybrid grey MCDM model. Sustainability, 12(17), 1-24. Triantaphyllou, E. (2000). Multi-criteria decision making methods: A comparative study. USA: Springer.
  • Tzeng, G.H., & Huang, J.J. (2011). Multiple attribute decision making: Methods and applications. CRC Press, Taylor & Francis Group, A Chapman&Hall.
  • Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2016, April). Normalization techniques for multi-criteria decision making: analytical hierarchy process case study. In doctoral conference on computing, electrical and industrial systems (pp. 261-269). Springer, Cham.
  • Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2020). In Camarinha-Matos, L.M., Farhadi, N., Lopes, F., Pereira, H.R. (Eds.), Selecting normalization techniques for the analytical hierarchy process (pp. 43-52). Portugal: DOCEIS.
  • Wang, Y. M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51(1- 2):1-12.
  • Wu, H.H. (2002). A comparative study of using grey relational analysis in multiple attribute decision making problems. Quality Engineering, 15(2), 209-217.
  • Yazdani, M., Jahan, A., & Zavadskas, E. (2017). Analysis in material selection: ınfluence of normalization tools on copras-g. Economic Computation & Economic Cybernetics Studies & Research, 51(1): 59-74.
  • Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9): 2501-2519.
  • Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multi-criteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1(3): 131-139.
  • Zavadskas, E.K., & Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica, 19: 303–314.
  • Zeng, Q.L., Li, D.D., & Yang ,Y. B. (2013). VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management. Journal of Medical System, 37: 1-9.
  • Zietsman, J., Rilett, L. R., & Kim, S.-J. (2006). Transportation corridor decision-making with multi-attribute utility theory. International Journal of Management and Decision Making, 7(2), 254-266.

NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS

Yıl 2021, Cilt: 22 Sayı: 2, 217 - 234, 04.02.2022
https://doi.org/10.24889/ifede.974252

Öz

Following the creation of the decision matrix, the first step in MCDM methods is the normalization process. Normalization is one of the most important processes in MCDM methods, and it has an effect on MCDM ranking results. Therefore, choosing the appropriate normalization technique is very important in decision problems. This study aims to reveal the effect of normalization techniques on CoCoSo method results under different scenarios and select a suitable normalization technique. The study determined that N3, N4 and N6 normalization techniques can be used as alternatives to the max min normalization technique in the algorithm of the CoCoSo method. It was also determined that N1 and N2 normalization techniques are not suitable for the CoCoSo method. In this study, the suitability of different normalization techniques for the CoCoSo method was tested for the first time.

Kaynakça

  • Abdel-Basset, M., Ding, W., Mohamed, R., & Metawa, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing ındustries. Risk Management, 22(3), 192-218.
  • Asgharpour, M. J. (1998). Multiple criteria decision making. Tehran: Tehran University Press.
  • Brans, J. P., & Vincke, P. (1985). A preference ranking organisation method (the promethee method for multiple criteria decision-making). Management Science, 31(6), 647-656.
  • Brauers, W.K., & Zavadskas, E.K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 443–468.
  • Brauers, W.K.M., & Zavadskas, E.K. (2009). Robustness of the multi-objective MOORA method with a test for the facilities sector. Technological and Economic Development of Economy: Baltic J on Sustainability, 15: 352-375.
  • Celen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: With an application to turkish deposit banking market. Informatica, 25(2), 185-208.
  • Chakraborty, S., & Yeh, C. H. (2007). A simulation based comparative study of normalization procedures in multiattribute decision making. 6th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, 102-109.
  • Chakraborty, S., & Yeh, C.H. (2009). A simulation comparison of normalization procedures for TOPSIS. Computing Industrial Engineering, 5(9):1815–1820.
  • Chakraborty, S., & Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25 (1): 1–20.
  • Chatterjee, P., & Chakraborty, S. (2014). Investigating the effect of normalization norms in flexible manufacturing sytem selection using multi-criteria decision-making methods. Journal of Engineering Science & Technology Review, 7(3): 141-150.
  • Delft, A. D., & Nijkamp, P. (1977). Multi-Criteria analysis and regional decision-making. Springer Science & Business Media, Berlin, Germany.
  • Durucasu, H., Aytekin, A., Saraç, B., & Orakçı, E (2017). Current application fields of ELECTRE and PROMETHEE: A literature review. Alphanumeric Journal, 5(2), 229-270.
  • Ersoy, N. (2020). Selecting the best normalization technique for ROV method: Towards a real life application. Gazi University Journal of Science. 34(2):592-609.
  • Farag, M. M. (1997). Materials selection for engineering design. USA: Prentice Hall.
  • Ghorabaee, M. K., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision making. Economic Computation and Economic Cybernetics Studies and Research, 3(50), 25-44.
  • Huang, W. C., & Chen, C. H. (2005). Using the ELECTRE II method to apply and analyze the differentation theory. Proceedings of the Eastern Asia Society for Transportation Studies, 2237-2249.
  • Jahan, A., & Edwards, K. L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials & Design, 65, 335-342.
  • Jahan, A., Bahraminasab, M., & Edwards, K. L. (2012). A target-based normalization technique for materials selection. Materials & Design, 35, 647-654. Jee, D. H., & Kang, K. J. (2000). A method for optimal material selection aided with decision making theory. Materials & Design, 21(3): 199-206.
  • Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). Simultaneous evaluation of criteria and alternatives (SECA) for multi-criteria decision-making. Informatica, 29(2), 265-280.
  • Kosareva, N., Krylovas, A., & Zavadskas, E. K. (2018). Statistical analysis of MCDM data normalization methods using monte carlo approach. The Case of Ternary Estimates Matrix. Economic Computation and Economic Cybernetics Studies and Research, 52, 159-175.
  • Krishankumar, R., Premaladha, J., Ravichandran, K. S., Sekar, K. R., Manikandan, R., & Gao, X. Z. (2020). A novel extension to VIKOR method under intuitionistic fuzzy context for solving personnel selection problem. Soft Computing, 24(2), 1063-1081.
  • Lai, Y.J., & Hwang, C.L. (1994). Fuzzy multiple objective decision making: methods and applications. Berlin: Springer-Verlag.
  • Lakshmi, T. M., & Venkatesan, V. P. (2014). A comparison of various normalization in techniques for order performance by similarity to ideal solution (TOPSIS). International Journal of Computing Algorithm, 3, 882-888.
  • Madić, M., & Radovanović, M. (2015). Ranking of some most commonly used non-traditional machining processes using ROV and CRITIC methods. UPB Scientific bulletin, Series D: Mechanical Engineering, 77(2), 193-204.
  • Mahmoudi, A., Deng, X., Javed, S. A., & Yuan, J. (2020). Large-scale multiple criteria decision-making with missing values: Project selection through TOPSIS-OPA. Journal of Ambient Intelligence and Humanized Computing, 1-22.
  • Mathew, M., Sahu, S., & Upadhyay, A. K. (2017). Effect of normalization techniques in robot selection using weighted aggregated sum product assessment. International journal of innovative research and advanced studies, 4(2):59-63.
  • Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A. (2005). The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural and Multidisciplinary Optimization, 29(4), 312-318.
  • Mufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438.
  • Opricovic, S., & Tzeng, G.H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur J Oper Res,156:445–55.
  • Özdağoğlu, A. (2013). The effects of different normalization methods to decision making process in TOPSIS. Ege Academic Review, 13(2), 245-258.
  • Pavlicic, D. (2001). Normalization affects the results of MADM methods. Yugoslav Journal of Operations Research, 11(2): 251–265.
  • Peldschus, F., Vaigauskas, E., & Zavadskas, E. K. (1983). Technologische entscheidungen bei der berücksichtigung mehrerer ziehle. Bauplanung Bautechnik, 37(4): 173-175.
  • Saaty, T. L. (1980).The analytic hierarchy process. New York: McGraw-Hill.
  • Shanian, A., & Savadogo, O. (2006). TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of Power Sources, 159(2): 1095-1104.
  • Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An Extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8): 801-813.
  • Stanujkic, D., Dordevic, B., & Dordevic, M. (2013). Comparative analysis of some prominent MCDM methods: a case of ranking serbian banks. Serbian Journal of Management, 8(2): 213-241.
  • Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare ındustries using a new MCDM method: measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140 (106231), 1-15.
  • Tabucanon, M.T. (1988). Multiple criteria decision making in industry. Elsevier, Amsterdam, The Netherlands.
  • Tadić, S., Krstić, M., Roso, V., & Brnjac, N. (2020). Dry port terminal location selection by applying the hybrid grey MCDM model. Sustainability, 12(17), 1-24. Triantaphyllou, E. (2000). Multi-criteria decision making methods: A comparative study. USA: Springer.
  • Tzeng, G.H., & Huang, J.J. (2011). Multiple attribute decision making: Methods and applications. CRC Press, Taylor & Francis Group, A Chapman&Hall.
  • Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2016, April). Normalization techniques for multi-criteria decision making: analytical hierarchy process case study. In doctoral conference on computing, electrical and industrial systems (pp. 261-269). Springer, Cham.
  • Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2020). In Camarinha-Matos, L.M., Farhadi, N., Lopes, F., Pereira, H.R. (Eds.), Selecting normalization techniques for the analytical hierarchy process (pp. 43-52). Portugal: DOCEIS.
  • Wang, Y. M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51(1- 2):1-12.
  • Wu, H.H. (2002). A comparative study of using grey relational analysis in multiple attribute decision making problems. Quality Engineering, 15(2), 209-217.
  • Yazdani, M., Jahan, A., & Zavadskas, E. (2017). Analysis in material selection: ınfluence of normalization tools on copras-g. Economic Computation & Economic Cybernetics Studies & Research, 51(1): 59-74.
  • Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9): 2501-2519.
  • Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multi-criteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1(3): 131-139.
  • Zavadskas, E.K., & Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica, 19: 303–314.
  • Zeng, Q.L., Li, D.D., & Yang ,Y. B. (2013). VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management. Journal of Medical System, 37: 1-9.
  • Zietsman, J., Rilett, L. R., & Kim, S.-J. (2006). Transportation corridor decision-making with multi-attribute utility theory. International Journal of Management and Decision Making, 7(2), 254-266.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Nazlı Ersoy 0000-0003-0011-2216

Yayımlanma Tarihi 4 Şubat 2022
Yayımlandığı Sayı Yıl 2021 Cilt: 22 Sayı: 2

Kaynak Göster

APA Ersoy, N. (2022). NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 22(2), 217-234. https://doi.org/10.24889/ifede.974252
AMA Ersoy N. NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. Şubat 2022;22(2):217-234. doi:10.24889/ifede.974252
Chicago Ersoy, Nazlı. “NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 22, sy. 2 (Şubat 2022): 217-34. https://doi.org/10.24889/ifede.974252.
EndNote Ersoy N (01 Şubat 2022) NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 22 2 217–234.
IEEE N. Ersoy, “NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS”, Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, c. 22, sy. 2, ss. 217–234, 2022, doi: 10.24889/ifede.974252.
ISNAD Ersoy, Nazlı. “NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 22/2 (Şubat 2022), 217-234. https://doi.org/10.24889/ifede.974252.
JAMA Ersoy N. NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2022;22:217–234.
MLA Ersoy, Nazlı. “NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, c. 22, sy. 2, 2022, ss. 217-34, doi:10.24889/ifede.974252.
Vancouver Ersoy N. NORMALIZATION PROCEDURES FOR COCOSO METHOD: A COMPARATIVE ANALYSIS UNDER DIFFERENT SCENARIOS. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2022;22(2):217-34.
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