Research Article
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Comparison of the Performances of MCDM Methods under Uncertainty: An Analysis on Bist SME Industry Index

Year 2022, Volume 19, Issue 46, 308 - 326, 30.03.2022
https://doi.org/10.26466/opusjsr.1064280

Abstract

MCDM is a sort of ranking and selection methodology widely used both in daily life and in disciplines such as social, science, health, informatics, and engineering. However, the selection of an appropriate MCDM method is a common and chronic problem of these disciplines. Because the issue of determining the most appropriate method among MCDM methods has not been clarified yet. Since the algorithms of more than a hundred MCDM methods currently that are in use are different, the ranking they produce or the "best alternative" often varies. Although all these methods claim to suggest the best alternative, it is unclear which method should be chosen for the decision maker. In fact, it can be said that input capabilities are focused more in the selection of MCDM methods. On the other hand, besides the potential capabilities of MCDM methods, the results they produce are also important in comparison. In this direction, MCDM-based financial performance measurement of companies was made in this study. The performance of WSA and FUCA methods was evaluated according to Spearman rho and entropy values. Accordingly, the method with the highest capacity is clearly FUCA, because this method showed a clearly higher performance in 10 of 12 problems/terms according to both criteria.

References

  • Ağ, A. R., Kuloğlu E. (2020). İşletmelerin finansal performansının veri zarflama analizi yöntemiyle tespit edilmesi: borsa istanbul’da işlem gören enerji işletmelerine yönelik bir uygulama. OPUS Uluslararası Toplum Araştırmaları Dergisi, 16(29 Ekim Özel Sayısı), 3756-3772.
  • Ayhan, E. and Önder, M. (2021). İnsan kaynaklarının kurumsal performansa etkisi: Gençlik STK’ları üzerine ampirik bir araştırma. Sosyoekonomi, 29(48), 443-472.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
  • Ban, A. I., Ban, O. I., Bogdan, V., Popa, D. C. S. and Tuse, D. (2020). Performance evaluation model of Romanian manufacturing listed companies by fuzzy AHP and TOPSIS. Technological and Economic Development of Economy, 26(4), 808-836.
  • Baydaş, M. and Elma, O. E. (2021). An objective criteria proposal for the comparison of MCDM and weighting methods in financial performance measurement: An application in Borsa Istanbul. Decision Making: Applications in Management and Engineering, 4(2), 257-279.
  • Baydaş, M. and Eren, T. (2021). Finansal performans ölçümünde ÇKKV yöntem seçimi problemine objektif bir yaklaşım: Borsa İstanbul'da bir uygulama. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(3), 664-687.
  • Baydaş, M., Elma, O. E. and Pamučar, D. (2022). Exploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial markets. Expert Systems with Applications, Volume 197, 116755.
  • Brigham, E. F. and Houston, J. F., 2019. Fundamentals of financial management (15th Ed.). Boston: Cengage Learning.
  • Carton, R. B. (2004). Measuring organizational performance: An exploratory study. Doctoral dissertation. University of Georgia.
  • Chen, Y. and Qu, L. (2006). Evaluating the selection of logistics centre location using fuzzy MCDM model based on entropy weight. 2006 6th World Congress on Intelligent Control and Automation, p.7128-7132).
  • Danesh, D., Ryan, M. J. and Abbasi, A. (2017). A systematic comparison of multi-criteria decision making methods for the improvement of project portfolio management in complex organisations. International Journal of Management and Decision Making, 16(3), 280-320.
  • De Almeida-Filho, A. T., De Lima Silva, D. F. and Ferreira L. (2020). Financial modelling with multiple criteria decision making: A systematic literature review. Journal of the Operational Research Society, 72(10), 2161-2179.
  • Diakoulaki, D., Mavrotas, G. and Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Eldrandaly, K., Ahmed, A. H. and AbdelAziz, N. (2009). An expert system for choosing the suitable MCDM method for solving a spatial decision problem. 9th International conference on production engineering, design and control.
  • Ertuğrul, İ. and Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715.
  • Feng, C. M. and Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142.
  • Gümüş, U. T., Öziç, H. C. and Sezer, D. (2019). BİST’te inşaat ve bayındırlık sektöründe işlem gören işletmelerin SWARA ve ARAS yöntemleriyle finansal performanslarının değerlendirilmesi. OPUS Uluslararası Toplum Araştırmaları Dergisi, 10(17), 835-858.
  • Karaoğlan, S. and Şahin, S. (2018). BİST XKMYA işletmelerinin finansal performanslarının çok kriterli karar verme yöntemleri ile ölçümü ve yöntemlerin karşılaştırılması. Ege Akademik Bakış Dergisi, 18(1), 63-80.
  • Karakul, A. K. and Özaydin, G. (2019). Topsis Ve Vikor Yöntemleri ile finansal performans değerlendirmesi: XELKT üzerinde bir uygulama. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 60, 68-86.
  • Kashid, U. S., Kashid, D. and Mehta, S. N. (2019). A Review of Mathematical Multi-Criteria Decision Models with A case study. International Conference on Efficacy of Software Tools for Mathematical Modeling (ICESTMM’19).
  • Mendoza, M. Luis Fernando, J. L. Perez Escobedo, C. Azzaro-Pantel, L. Pibouleau, S.Domenech, and A. Aguilar-Lasserre (2011). Selecting The Best Portfolio Alternative from A Hybrid Multiobjective GA-MCDM Approach for New Product Development in the Pharmaceutical Industry. 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), Paris, p.159–166.
  • Mota, P., Campos, A. R. and Neves-Silva, R. (2012). First look at MCDM: Choosing a decision method. Advances in Smart Systems Research, 3(1), 25.
  • Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76-105.
  • Munier, N. (2021). Retrieved from https://www.researchgate.net/post/How_can_the_performance_of_fuzzy_sets_compare_in_fuzzy_multi-criteria_decision_making on 25.01.2022.
  • Munier, N. (2006). Economic growth and sustainable development: Could multicriteria analysis be used to solve this dichotomy?. Environment, Development and Sustainability, 8, 425–443.
  • Li, X., Wang, K., Liu, L., Xin, J., Yang, H., and Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085-2091.
  • Pineda, P. J. G., Liou, J. J., Hsu, C. C. and Chuang, Y. C. (2018). An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management, 68, 103-117.
  • Sałabun, W. and Urbaniak, K. (2020). A new coefficient of rankings similarity in decision-making problems. International Conference on Computational Science (p.632-645). Springer, Cham.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
  • Shen, K. Y. and Tzeng, G. H. (2016). Combining DRSA decision-rules with FCA-based DANP evaluation for financial performance improvements. Technological and Economic Development of Economy, 22(5), 685-714.
  • Stewart, B. (2013). Best-practice EVA: the definitive guide to measuring and maximizing shareholder value. John Wiley & Sons.
  • Şen, S. (2014). Farklı ağırlıklandırma tekniklerinin denendiği çok kriterli karar verme yöntemleri ile Türkiye’deki mevduat bankalarının mali performans değerlendirmesi. Yayınlanmamış Yüksek Lisans Tezi. Mimar Sinan Güzel Sanatlar Üniversitesi, Fen Bilimleri Enstitüsü / İstatistik Ana Bilim Dalı, İstanbul.
  • Ozernoy, V. M. (1992). Choosing the “Best” multiple criterlv decision-making method. INFOR: Information Systems and Operational Research, 30(2), 159-171.
  • Taşabat, S. E., Cinemre, N. and Şen, S. (2015). Farklı ağırlıklandırma tekniklerinin denendiği çok kriterli karar verme yöntemleri ile Türkiye’deki mevduat bankalarının mali performanslarının değerlendirilmesi. Sosyal Bilimler Araştırma Dergisi, 4(2), 96-110.
  • Tavana, M., Khalili-Damghani, K. and Rahmatian, R. (2015). A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies. Annals of Operations Research, 226(1), 589-621.
  • Tavana, M. (2021). Decision analytics in the world of big data and colorful choices. Decision Analytics Journal, 1, 100002.
  • Triantaphyllou, E. (2000). Multi criteria decision making methods: A comparative study. London: Kluwer Academic Publishers.
  • Wang, Z. and Rangaiah, G. P. (2017). Application and analysis of methods for selecting an optimal solution from the Pareto-optimal front obtained by multiobjective optimization. Industrial & Engineering Chemistry Research, 56(2), 560-574.
  • Wang, Z., Parhi, S. S., Rangaiah, G. P., Jana, A. K. (2020). Analysis of weighting and selection methods for pareto-optimal solutions of multiobjective optimization in chemical engineering applications. Industrial & Engineering Chemistry Research 59(33), 14850-14867.
  • Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A. and Zioło, M. (2019). Generalised framework for multi-criteria method selection. Omega, 86, 107-124.
  • Wu, J., Sun, J., Liang, L. and Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165.
  • Yalçın, N., Bayrakdaroglu, A. and Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert systems with applications, 39(1), 350-364.
  • Yükçü, S. and Atağan, G. (2010). TOPSIS yöntemine göre performans değerleme. Muhasebe ve Finansman Dergisi, 45, 28-35.
  • Zaidan, B. B., Zaidan, A. A., Abdul Karim, H. and Ahmad, N. N. (2017). A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology & Decision Making, 16, 1-42.
  • Zopounidis, C. and Doumpos, M. (2002). Multi‐criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi‐Criteria Decision Analysis, 11(4‐5), 167-186.
  • Zhu, Y., Tian, D. and Yan, F. (2020). Effectiveness of entropy weight method in decision-making. Mathematical Problems in Engineering, Volume 2020, ArticleID:3564

Year 2022, Volume 19, Issue 46, 308 - 326, 30.03.2022
https://doi.org/10.26466/opusjsr.1064280

Abstract

References

  • Ağ, A. R., Kuloğlu E. (2020). İşletmelerin finansal performansının veri zarflama analizi yöntemiyle tespit edilmesi: borsa istanbul’da işlem gören enerji işletmelerine yönelik bir uygulama. OPUS Uluslararası Toplum Araştırmaları Dergisi, 16(29 Ekim Özel Sayısı), 3756-3772.
  • Ayhan, E. and Önder, M. (2021). İnsan kaynaklarının kurumsal performansa etkisi: Gençlik STK’ları üzerine ampirik bir araştırma. Sosyoekonomi, 29(48), 443-472.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
  • Ban, A. I., Ban, O. I., Bogdan, V., Popa, D. C. S. and Tuse, D. (2020). Performance evaluation model of Romanian manufacturing listed companies by fuzzy AHP and TOPSIS. Technological and Economic Development of Economy, 26(4), 808-836.
  • Baydaş, M. and Elma, O. E. (2021). An objective criteria proposal for the comparison of MCDM and weighting methods in financial performance measurement: An application in Borsa Istanbul. Decision Making: Applications in Management and Engineering, 4(2), 257-279.
  • Baydaş, M. and Eren, T. (2021). Finansal performans ölçümünde ÇKKV yöntem seçimi problemine objektif bir yaklaşım: Borsa İstanbul'da bir uygulama. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(3), 664-687.
  • Baydaş, M., Elma, O. E. and Pamučar, D. (2022). Exploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial markets. Expert Systems with Applications, Volume 197, 116755.
  • Brigham, E. F. and Houston, J. F., 2019. Fundamentals of financial management (15th Ed.). Boston: Cengage Learning.
  • Carton, R. B. (2004). Measuring organizational performance: An exploratory study. Doctoral dissertation. University of Georgia.
  • Chen, Y. and Qu, L. (2006). Evaluating the selection of logistics centre location using fuzzy MCDM model based on entropy weight. 2006 6th World Congress on Intelligent Control and Automation, p.7128-7132).
  • Danesh, D., Ryan, M. J. and Abbasi, A. (2017). A systematic comparison of multi-criteria decision making methods for the improvement of project portfolio management in complex organisations. International Journal of Management and Decision Making, 16(3), 280-320.
  • De Almeida-Filho, A. T., De Lima Silva, D. F. and Ferreira L. (2020). Financial modelling with multiple criteria decision making: A systematic literature review. Journal of the Operational Research Society, 72(10), 2161-2179.
  • Diakoulaki, D., Mavrotas, G. and Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
  • Eldrandaly, K., Ahmed, A. H. and AbdelAziz, N. (2009). An expert system for choosing the suitable MCDM method for solving a spatial decision problem. 9th International conference on production engineering, design and control.
  • Ertuğrul, İ. and Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715.
  • Feng, C. M. and Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142.
  • Gümüş, U. T., Öziç, H. C. and Sezer, D. (2019). BİST’te inşaat ve bayındırlık sektöründe işlem gören işletmelerin SWARA ve ARAS yöntemleriyle finansal performanslarının değerlendirilmesi. OPUS Uluslararası Toplum Araştırmaları Dergisi, 10(17), 835-858.
  • Karaoğlan, S. and Şahin, S. (2018). BİST XKMYA işletmelerinin finansal performanslarının çok kriterli karar verme yöntemleri ile ölçümü ve yöntemlerin karşılaştırılması. Ege Akademik Bakış Dergisi, 18(1), 63-80.
  • Karakul, A. K. and Özaydin, G. (2019). Topsis Ve Vikor Yöntemleri ile finansal performans değerlendirmesi: XELKT üzerinde bir uygulama. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 60, 68-86.
  • Kashid, U. S., Kashid, D. and Mehta, S. N. (2019). A Review of Mathematical Multi-Criteria Decision Models with A case study. International Conference on Efficacy of Software Tools for Mathematical Modeling (ICESTMM’19).
  • Mendoza, M. Luis Fernando, J. L. Perez Escobedo, C. Azzaro-Pantel, L. Pibouleau, S.Domenech, and A. Aguilar-Lasserre (2011). Selecting The Best Portfolio Alternative from A Hybrid Multiobjective GA-MCDM Approach for New Product Development in the Pharmaceutical Industry. 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), Paris, p.159–166.
  • Mota, P., Campos, A. R. and Neves-Silva, R. (2012). First look at MCDM: Choosing a decision method. Advances in Smart Systems Research, 3(1), 25.
  • Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76-105.
  • Munier, N. (2021). Retrieved from https://www.researchgate.net/post/How_can_the_performance_of_fuzzy_sets_compare_in_fuzzy_multi-criteria_decision_making on 25.01.2022.
  • Munier, N. (2006). Economic growth and sustainable development: Could multicriteria analysis be used to solve this dichotomy?. Environment, Development and Sustainability, 8, 425–443.
  • Li, X., Wang, K., Liu, L., Xin, J., Yang, H., and Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Engineering, 26, 2085-2091.
  • Pineda, P. J. G., Liou, J. J., Hsu, C. C. and Chuang, Y. C. (2018). An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management, 68, 103-117.
  • Sałabun, W. and Urbaniak, K. (2020). A new coefficient of rankings similarity in decision-making problems. International Conference on Computational Science (p.632-645). Springer, Cham.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
  • Shen, K. Y. and Tzeng, G. H. (2016). Combining DRSA decision-rules with FCA-based DANP evaluation for financial performance improvements. Technological and Economic Development of Economy, 22(5), 685-714.
  • Stewart, B. (2013). Best-practice EVA: the definitive guide to measuring and maximizing shareholder value. John Wiley & Sons.
  • Şen, S. (2014). Farklı ağırlıklandırma tekniklerinin denendiği çok kriterli karar verme yöntemleri ile Türkiye’deki mevduat bankalarının mali performans değerlendirmesi. Yayınlanmamış Yüksek Lisans Tezi. Mimar Sinan Güzel Sanatlar Üniversitesi, Fen Bilimleri Enstitüsü / İstatistik Ana Bilim Dalı, İstanbul.
  • Ozernoy, V. M. (1992). Choosing the “Best” multiple criterlv decision-making method. INFOR: Information Systems and Operational Research, 30(2), 159-171.
  • Taşabat, S. E., Cinemre, N. and Şen, S. (2015). Farklı ağırlıklandırma tekniklerinin denendiği çok kriterli karar verme yöntemleri ile Türkiye’deki mevduat bankalarının mali performanslarının değerlendirilmesi. Sosyal Bilimler Araştırma Dergisi, 4(2), 96-110.
  • Tavana, M., Khalili-Damghani, K. and Rahmatian, R. (2015). A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies. Annals of Operations Research, 226(1), 589-621.
  • Tavana, M. (2021). Decision analytics in the world of big data and colorful choices. Decision Analytics Journal, 1, 100002.
  • Triantaphyllou, E. (2000). Multi criteria decision making methods: A comparative study. London: Kluwer Academic Publishers.
  • Wang, Z. and Rangaiah, G. P. (2017). Application and analysis of methods for selecting an optimal solution from the Pareto-optimal front obtained by multiobjective optimization. Industrial & Engineering Chemistry Research, 56(2), 560-574.
  • Wang, Z., Parhi, S. S., Rangaiah, G. P., Jana, A. K. (2020). Analysis of weighting and selection methods for pareto-optimal solutions of multiobjective optimization in chemical engineering applications. Industrial & Engineering Chemistry Research 59(33), 14850-14867.
  • Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A. and Zioło, M. (2019). Generalised framework for multi-criteria method selection. Omega, 86, 107-124.
  • Wu, J., Sun, J., Liang, L. and Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165.
  • Yalçın, N., Bayrakdaroglu, A. and Kahraman, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert systems with applications, 39(1), 350-364.
  • Yükçü, S. and Atağan, G. (2010). TOPSIS yöntemine göre performans değerleme. Muhasebe ve Finansman Dergisi, 45, 28-35.
  • Zaidan, B. B., Zaidan, A. A., Abdul Karim, H. and Ahmad, N. N. (2017). A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques. International Journal of Information Technology & Decision Making, 16, 1-42.
  • Zopounidis, C. and Doumpos, M. (2002). Multi‐criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi‐Criteria Decision Analysis, 11(4‐5), 167-186.
  • Zhu, Y., Tian, D. and Yan, F. (2020). Effectiveness of entropy weight method in decision-making. Mathematical Problems in Engineering, Volume 2020, ArticleID:3564

Details

Primary Language English
Subjects Social
Journal Section Research Articles
Authors

Mahmut BAYDAŞ (Primary Author)
Necmettin Erbakan University
0000-0001-6195-667X
Türkiye

Supporting Institution yok
Project Number yok
Early Pub Date March 25, 2022
Publication Date March 30, 2022
Published in Issue Year 2022, Volume 19, Issue 46

Cite

APA Baydaş, M. (2022). Comparison of the Performances of MCDM Methods under Uncertainty: An Analysis on Bist SME Industry Index . OPUS Journal of Society Research , 19 (46) , 308-326 . DOI: 10.26466/opusjsr.1064280