Ekonomik Göstergelere Dayalı Tedarikçi Seçimi için Sezgisel Bulanık Yaklaşım

Yıl 2021, Cilt: 23 Sayı: 3, 1017 - 1037, 31.12.2021

Ayşenur Akın Vargeloğlu , Mükerrem Bahar Başkır , Hamza Gamgam

https://doi.org/10.26745/ahbvuibfd.906659

Cited By: 3

Öz

Artan rekabet ortamında varlığını sürdürmek isteyen işletmeler için tedarikçi seçim ve değerlendirmeleri önemli bir yere sahiptir. İşletmeler uygun tedarikçi seçimi ile müşteri beklentilerini en iyi şekilde karşılamayı hedeflemektedir. Bu çalışmada, tedarikçi seçimi ve değerlendirmeleri için sezgisel bulanık kümelere dayalı bütünleşik bir yaklaşım önerilmektedir. Önerilen yaklaşım ile karar verici algı farklılıkları kaynaklı belirsizlikler ait olma ve ait olmama dereceleri ile detaylı şekilde incelenmektedir. Bu yaklaşım, inşaat sektöründe faaliyet gösteren bir işletmenin tedarikçi seçimi ve değerlendirmelerine uygulanmıştır. Uygulamada, ekonomik kriterlere dayalı karar verici öznel değerlendirmeleri ile işletme mevcut değerlendirme puanları birleştirilerek karma veritabanı oluşturulmuştur. Klasik ve bulanık öbekleme yaklaşımları ile tedarikçi firma sıralama ve sınıflandırmaları elde edilmiştir. Karma veritabanı için bulanık öbekleme yapısı %99,1; klasik öbekleme yapısı ise %94,4 doğruluk oranı ile oluşmuştur. Ayrıca, bulanık ve klasik öbekleme sonuçları içerisinde sadece tedarikçi değerlendirme puanlarına ait öbeklemenin, sırasıyla, %90,7 ve %74,1 doğruluk oranına sahip olduğu görülmüştür. Algı farklılıklarının tedarikçi seçimine yönelik karar almadaki etkileri önerilen bütünleşik yaklaşımla açıklanmıştır.

Anahtar Kelimeler

sezgisel bulanık küme, tedarikçi seçimi, belirsizlik, bulanık öbek ortalamaları, k-ortalamalar

Intuitionistic Fuzzy Approach for Supplier Selection Based on Economic Indicators

Öz

Supplier selection and evaluation is an important issue for organizations to cope with increasing competitive environment. Companies aim to meet customer expectations with selection of appropriate supplier. In this study, an integrated approach based on intuitionistic fuzzy sets is proposed for supplier selection and evaluation. In the proposed approach, uncertainties arising from discrepancies in decision-makers' perception are examined in detail with belonging and non-belonging degrees. This approach was applied to supplier selection and evaluations of a construction company. In practice, a mixed database was created by combining the subjective evaluations of decision makers based on economic criteria and current evaluation scores of the company. Supplies were sorted and classified using the classical and fuzzy clustering approaches. For the mixed database, fuzzy and classical clustering structures were created with 99.1% and 94.4% accuracy rates, respectively. Besides, within the fuzzy and classical clustering-results, classification-accuracy rates of only supplier evaluation scores were found as 90.7% and 74.1%, respectively. The effects of perception discrepancies on decision making for supplier selection were explained by means of the proposed integrated approach.

Anahtar Kelimeler

Intuitionistic fuzzy set, supplier selection, uncertainty, fuzzy c-means, k-means

Kaynakça

• Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96.
• Atanassov, K. (1999). Intuitionistic fuzzy sets: theory and applications. Heidelberg, Germany: Physica-Verlag, 1-50.
• Arefi, M. and Taheri, S. M. (2015). Least-squares regression based on Atanassov’s intuitionistic fuzzy inputs outputs and Atanassov’s intuitionistic fuzzy parameters. IEEE Transactions on Fuzzy Systems, 23(4), 1142-1154.
• Başkır, M. B. (2011). Bulanık kalite fonksiyon yayılımı yaklaşımının iyileştirilmesi ve uygulamaları. Doktora Tezi, Ankara Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
• Başkır, M. B. (2017). 4-Aşamalı Bulanık Kalite Fonksiyon Yayılımı Yaklaşımı ile Tedarikçi Seçimi, Verimlilik Dergisi, 2017/4, 81-110.
• Bezdek, J.C. 1981. Pattern recognition with fuzzy objective function algorithms. Plenum Press, 256 p., New York.
• Boran, F. E., S. Genc, M. Kurt and D. Akay (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36, 11363–11368.
• Boran, F. E. (2011). An integrated intuitionistic fuzzy multicriteria decision making method for facility location selection. Mathematical and Computational Applications, 16 (2), 487–496.
• Chang, K.H. (2019). A novel supplier selection method that integrates the intuitionistic fuzzy weighted averaging method and a soft set with imprecise data. Annals of Operations Research, 272, 139–157.
• Dickson, G. W. (1966). An analysis of vendor selection system and decisions. Journal of purchasing, 2(1), 28–41.
• Dubois, D. and Prade, H. (1980). Fuzzy sets and systems: Theory and application. United States of America: Academic Press, 57-65.
• Guo, Z., Qi, M., and Zhao, X. (2010). A new approach based on intuitionistic fuzzy set for selection of suppliers, Proc. of 6th International Conference on Natural Computation (ICNC 2010), 3715–3718.
• Guo C., Li X. (2014). A multi-echelon inventory system with supplier selection and order allocation under stochastic demand. International Journal of Production Economics, 151, 37-47.
• Hand, D., Mannila H. et.al. (2001). Principles of data mining, USA: Massachusetts Instittute of Technology.
• Ho, W., Xiaowei X., and Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1),19-24.
• Kaur, P. (2014a). An Intuitionistic Fuzzy Multi-Objective Vendor Selection Problem. Applied Mathematical Sciences, 8(149), 7443–7452.
• Kaur, P. (2014b). Selection of Vendor Based on Intuitionistic Fuzzy Analytical Hierarchy Process, Advances in Operations Research, ss-10.
• Kaur, P. and Pal. M. (2015). Selection of Vendor Based on Intuitionistic Fuzzy Linguistic Hedge. Proc. of 6th International IFS Conference, Mersin,Turkey, 69-75.
• Klir, G., Yuan, B. (1993). Fuzzy sets and fuzzy logic: theory and its applications. New York: Jersey Prentice Hall, 50-11.
• Liu, H.W., Wang, G. J. (2007). Multi-criteria decision-making methods based onintuitionistic fuzzy sets. European Journal of Operational Research 179, 220–233.
• Liu, A., Xiao. Y., Lu, H., BingTsai, S. and Song., W. (2019). A fuzzy three-stage multi-attribute decision-making approach based on customer needs for sustainable supplier selection. Journal of Cleaner Production, 239(1), 118043.
• MacQueen, J.B., (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proc. Symp. Math. Statist. and Probability (5th), 281-297.
• Mendoza, A., Santiago, E. and RaviRavindran, A. (2008). A three phase multicriteria method to the supplier selection problem. International Journal of Industrial Engineering, 15(2), 195–210.
• Nakipoğlu, N. and Bulgurcu, B. (2020). Supplier selection in a Turkish textile company by using intuitionistic fuzzy decision-making. The Journal of the Textile Institute, 112(2).
• Öztürk, M. and Tüksoy, T. (2020). Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(3),1519-1535.
• Pedrycz, W. (1989). Fuzzy control and fuzzy systems. Taunton: Research Studies Press John Wiley & Sons, 225 p, Chichester.
• Sotirova, E., Shannon, A., Sotirov, S. and Krawczak, M. (2014). Intuitionistic fuzzy estimation of the doctoral comprehensive examination. Notes on Intuitionistic Fuzzy Sets, 20(2), 119–125.
• Szmidt, E. and Kacprzyk, J. (2001). Intuitionistic Fuzzy Sets in Some Medical Applications. Notes on Intuitionistic Fuzzy Sets, 7(4), 58–64.
• Türkşen, I. B. (2006). An Ontological and epistemological perspective of fuzzy set theory. Elsevier B.V., 510 p.
• Zadeh, L. A. (1965). Fuzzy sets. Information Control 8, 338-353.
• Zadeh, L. A. (2002). From computing with numbers to cumputing with words from manipulation of measurements to manipulation to perceptions. Int. J. Appl. Math. Comput. Sci., 12(3), 307–324.
• Xie, X. L. and Beni, G.A. (1991). Validity measure for fuzzy clustering. IEEE Trans. Pattern and Machine Intelligence, 3 (8), 841-846.