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An new expert interval type-2 hybrid fuzzy rule-based AHP system for supplier selection

Yıl 2020, Cilt: 35 Sayı: 3, 1519 - 1536, 07.04.2020
https://doi.org/10.17341/gazimmfd.494086

Öz

The main study area
of fuzzy decision making is decision making under uncertainty. Because there
are verbal values, not numerical values, regarding criteria, alternatives and
results which causes uncertainty in return.
The membership
functions of type-1 fuzzy set do not have any uncertainty related to
themselves. Whereas, the excessive arithmetic operations are required by type-2
fuzzy set in comparison with type-1 fuzzy set, type-2 fuzzy set may address
more uncertainty in the issue of defining the membership functions by
generalizing type-1 fuzzy sets and systems. A type-2 fuzzy set lets us
incorporate the uncertainty of membership functions into the fuzzy set theory. For
this reason, integrating MCDM problems with interval type-2 fuzzy numbers will
provide advantages in the decision-making process. On the other hand, a mixed
analysis of the decision-making process, which requires the use of human
sensitivity to reflect the influence level of the decision maker, can be
expressed as the fuzzy rule base. Analytic Hierarchy Process (AHP) is a widely
used multicriteria decision making (MCDM) that can take into account various
and conflicting criteria at the same time. And the AHP method is also a method
that allows decision makers to incorporate their personal preferences into the
solution process. Our objective is to develop an Interval Type-2 Fuzzy
Rule-Based AHP (AT2 BKT AHP ) method together with a new ranking method for
type-2 fuzzy sets. We will apply the proposed method comparatively with the interval
type-2 fuzzy AHP (IT2 FAHP) method to a supplier selection problem. At the end
of the study, supplier selection for ranking a visual expert system design was
made using C # programming language in ASP.NET environment.

Kaynakça

  • 1. Karakaşoğlu, N. (2008). Bulanık çok kriterli karar verme yöntemleri ve bir uygulama, Yüksek Lisans Tezi, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü, Denizli.
  • 2. Chen, C. T., & Huang, S. F. (2006). Order-fulfillment ability analysis in the supply-chain system with fuzzy operation times. International Journal of Production Economics, 101(1), 185-193.
  • 3. Ke, H., Cui, Z., Govindan, K., & Zavadskas, E. K. (2015). The impact of contractual governance and trust on EPC projects in construction supply chain performance. Inzinerine Ekonomika-Engineering Economics, 26(4), 349-363.
  • 4. 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.
  • 5. Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., & Diabat, A. (2013). Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner production, 47, 355-367.
  • 6. Önüt, S., Gülsün, B., Tuzkaya, U. R., & Tuzkaya, G. (2008). A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Information Sciences, 178(2), 485-500.
  • 7. Sanayei, A., Mousavi, S. F., Abdi, M. R., & Mohaghar, A. (2008). An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming. Journal of the Franklin institute, 345(7), 731-747.
  • 8. Kilic, H. S. (2013). An integrated approach for supplier selection in multi-item/multi-supplier environment. Applied Mathematical Modelling, 37(14-15), 7752-7763.
  • 9. Yazdani, M., Hashemkhani Zolfani, S., & Zavadskas, E. K. (2016). New integration of MCDM methods and QFD in the selection of green suppliers. Journal of Business Economics and Management, 17(6), 1097-1113.
  • 10. Rezaei, J., Fahim, P.B., Tavasszy, L., 2014. Supplier selection in the airline retail industry using a funnel methodology: conjunctive screening method and fuzzy AHP. Expert Syst. Appl. 41 (18), 8165e8179. http://dx.doi.org/10.1016/ j.eswa.2014.07.005.
  • 11. Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Zolfani, S. H. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728-3740.
  • 12. Omurca, S. I. (2013). An intelligent supplier evaluation, selection and development system. Applied Soft Computing, 13(1), 690-697.
  • 13. Shidpour, H., Shahrokhi, M., & Bernard, A. (2013). A multi-objective programming approach, integrated into the TOPSIS method, in order to optimize product design; in three-dimensional concurrent engineering. Computers & Industrial Engineering, 64(4), 875-885.
  • 14. Awasthi, A., Chauhan, S. S., & Omrani, H. (2011). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert systems with Applications, 38(10), 12270-12280.
  • 15. Şengül, Ü., Eren, M., Shiraz, S. E., Gezder, V., & Şengül, A. B. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy, 75, 617-625.
  • 16. Kahraman, C., Öztayşi, B., Sarı, İ. U., & Turanoğlu, E. (2014). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59, 48-57.
  • 17. Tseng, M. L., Lin, Y. H., Chiu, A. S., & Chen, C. Y. (2008). Fuzzy AHP approach to TQM strategy evaluation. Industrial Engineering & Management Systems, 7(1), 34-43.
  • 18. Wu, Z., & Chen, Y. (2007). The maximizing deviation method for group multiple attribute decision making under linguistic environment. Fuzzy Sets and Systems, 158(14), 1608-1617.
  • 19. Kahraman, C., Ruan, D., & Doǧan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135-153.
  • 20. Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making (pp. 289-486). Springer, Berlin, Heidelberg.
  • 21. L.A. Zadeh (1965)., Fuzzy sets, Inform. Control 8 (3), 338–353.
  • 22. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information sciences, 8(3), 199-249.
  • 23. Karnik, N. N., & Mendel, J. M. (2001). Operations on type-2 fuzzy sets. Fuzzy sets and systems, 122(2), 327-348.
  • 24. Linda, O., & Manic, M. (2011). Interval type-2 fuzzy voter design for fault tolerant systems. Information Sciences, 181(14), 2933-2950.
  • 25. Jammeh, E. A., Fleury, M., Wagner, C., Hagras, H., & Ghanbari, M. (2009). Interval type-2 fuzzy logic congestion control for video streaming across IP networks. IEEE Transactions on Fuzzy Systems, 17(5), 1123-1142.
  • 26. Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE transactions on fuzzy systems, 14(6), 808-821.
  • 27. Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
  • 28. Türk, S., John, R., & Özcan, E. (2014, September). Interval type-2 fuzzy sets in supplier selection. In Computational Intelligence (UKCI), 2014 14th UK Workshop on (pp. 1-7). IEEE.
  • 29. Chan, F. T., & Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35(4), 417-431.
  • 30. Di Martino, F., & Sessa, S. (2014). Type-2 interval fuzzy rule-based systems in spatial analysis. Information Sciences, 279, 199-212.

Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi

Yıl 2020, Cilt: 35 Sayı: 3, 1519 - 1536, 07.04.2020
https://doi.org/10.17341/gazimmfd.494086

Öz

Bulanık karar vermenin
ana çalışma alanı, belirsizlik altında karar vermektir. Çünkü belirsizliğe
neden olan kriterlere, alternatiflere ve sonuçlara ilişkin sayısal değerler
değil, sözel değerler mevcuttur. Tip-1 bulanık kümelerin üyelik işlevleri,
kendisiyle ilgili bir belirsizliğe sahip değildir. Oysa tip-1 bulanık kümelere göre
tip-2 bulanık kümeler ile aşırı aritmetik işlemlere ihtiyaç duyulurken; tip-2
bulanık kümeler, tip-1 bulanık kümeleri ve sistemleri yaygınlaştırarak üyelik
fonksiyonlarını tanımlama konusunda daha fazla belirsizliği ele alabilmektedir.
Tip-2 bulanık kümesi, üyelik fonksiyonlarının belirsizliğini bulanık küme
teorisine dahil etmemizi sağlar. Bu nedenle, çok kriterli karar verme (MCDM)
problemlerinin tip-2 bulanık sayılar ile entegre edilmesi karar verme sürecinde
avantajlar sağlayacaktır. Öte yandan, karar vericinin etki derecesini yansıtmak
için insan duyarlılığının kullanılmasını gerektiren karar verme sürecinin karma
bir analizi bulanık kural tabanı ile ifade edilebilir. Analitik Hiyerarşi
Süreci (AHP), aynı anda çeşitli ve çelişen kriterleri hesaba katan ve yaygın
bir şekilde kullanılan MCDM yöntemidir. Ve AHP yöntemi, aynı zamanda karar
vericilerin kişisel tercihlerini çözüm sürecine dahil etmelerini sağlayan bir
yöntemdir. Amacımız, tip-2 bulanık kümeleri için yeni bir sıralama yöntemi ile
birlikte bir Aralık Tip-2 Bulanık Kuralı Tabanlı AHP (AT2 BKT AHP ) yöntemini
geliştirmektir. Önerilen metodu, Aralık Tip-2 Bulanık AHP (IT2 FAHP) metodu ile
bir tedarikçi seçim problemine karşılaştırmalı olarak uygulayacağız. Ayrıca çalışma
sonucunda tedarikçi seçimi sıralaması için ASP.NET ortamında C# programlama
dili kullanılarak görsel bir uzman sistem tasarımı yapılmıştır.

Kaynakça

  • 1. Karakaşoğlu, N. (2008). Bulanık çok kriterli karar verme yöntemleri ve bir uygulama, Yüksek Lisans Tezi, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü, Denizli.
  • 2. Chen, C. T., & Huang, S. F. (2006). Order-fulfillment ability analysis in the supply-chain system with fuzzy operation times. International Journal of Production Economics, 101(1), 185-193.
  • 3. Ke, H., Cui, Z., Govindan, K., & Zavadskas, E. K. (2015). The impact of contractual governance and trust on EPC projects in construction supply chain performance. Inzinerine Ekonomika-Engineering Economics, 26(4), 349-363.
  • 4. 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.
  • 5. Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., & Diabat, A. (2013). Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner production, 47, 355-367.
  • 6. Önüt, S., Gülsün, B., Tuzkaya, U. R., & Tuzkaya, G. (2008). A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Information Sciences, 178(2), 485-500.
  • 7. Sanayei, A., Mousavi, S. F., Abdi, M. R., & Mohaghar, A. (2008). An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming. Journal of the Franklin institute, 345(7), 731-747.
  • 8. Kilic, H. S. (2013). An integrated approach for supplier selection in multi-item/multi-supplier environment. Applied Mathematical Modelling, 37(14-15), 7752-7763.
  • 9. Yazdani, M., Hashemkhani Zolfani, S., & Zavadskas, E. K. (2016). New integration of MCDM methods and QFD in the selection of green suppliers. Journal of Business Economics and Management, 17(6), 1097-1113.
  • 10. Rezaei, J., Fahim, P.B., Tavasszy, L., 2014. Supplier selection in the airline retail industry using a funnel methodology: conjunctive screening method and fuzzy AHP. Expert Syst. Appl. 41 (18), 8165e8179. http://dx.doi.org/10.1016/ j.eswa.2014.07.005.
  • 11. Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Zolfani, S. H. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728-3740.
  • 12. Omurca, S. I. (2013). An intelligent supplier evaluation, selection and development system. Applied Soft Computing, 13(1), 690-697.
  • 13. Shidpour, H., Shahrokhi, M., & Bernard, A. (2013). A multi-objective programming approach, integrated into the TOPSIS method, in order to optimize product design; in three-dimensional concurrent engineering. Computers & Industrial Engineering, 64(4), 875-885.
  • 14. Awasthi, A., Chauhan, S. S., & Omrani, H. (2011). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert systems with Applications, 38(10), 12270-12280.
  • 15. Şengül, Ü., Eren, M., Shiraz, S. E., Gezder, V., & Şengül, A. B. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy, 75, 617-625.
  • 16. Kahraman, C., Öztayşi, B., Sarı, İ. U., & Turanoğlu, E. (2014). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59, 48-57.
  • 17. Tseng, M. L., Lin, Y. H., Chiu, A. S., & Chen, C. Y. (2008). Fuzzy AHP approach to TQM strategy evaluation. Industrial Engineering & Management Systems, 7(1), 34-43.
  • 18. Wu, Z., & Chen, Y. (2007). The maximizing deviation method for group multiple attribute decision making under linguistic environment. Fuzzy Sets and Systems, 158(14), 1608-1617.
  • 19. Kahraman, C., Ruan, D., & Doǧan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135-153.
  • 20. Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making (pp. 289-486). Springer, Berlin, Heidelberg.
  • 21. L.A. Zadeh (1965)., Fuzzy sets, Inform. Control 8 (3), 338–353.
  • 22. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information sciences, 8(3), 199-249.
  • 23. Karnik, N. N., & Mendel, J. M. (2001). Operations on type-2 fuzzy sets. Fuzzy sets and systems, 122(2), 327-348.
  • 24. Linda, O., & Manic, M. (2011). Interval type-2 fuzzy voter design for fault tolerant systems. Information Sciences, 181(14), 2933-2950.
  • 25. Jammeh, E. A., Fleury, M., Wagner, C., Hagras, H., & Ghanbari, M. (2009). Interval type-2 fuzzy logic congestion control for video streaming across IP networks. IEEE Transactions on Fuzzy Systems, 17(5), 1123-1142.
  • 26. Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE transactions on fuzzy systems, 14(6), 808-821.
  • 27. Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
  • 28. Türk, S., John, R., & Özcan, E. (2014, September). Interval type-2 fuzzy sets in supplier selection. In Computational Intelligence (UKCI), 2014 14th UK Workshop on (pp. 1-7). IEEE.
  • 29. Chan, F. T., & Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35(4), 417-431.
  • 30. Di Martino, F., & Sessa, S. (2014). Type-2 interval fuzzy rule-based systems in spatial analysis. Information Sciences, 279, 199-212.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Müslüm Öztürk 0000-0003-1941-3115

Turan Paksoy

Yayımlanma Tarihi 7 Nisan 2020
Gönderilme Tarihi 9 Aralık 2018
Kabul Tarihi 9 Şubat 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 35 Sayı: 3

Kaynak Göster

APA Öztürk, M., & Paksoy, T. (2020). Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(3), 1519-1536. https://doi.org/10.17341/gazimmfd.494086
AMA Öztürk M, Paksoy T. Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi. GUMMFD. Nisan 2020;35(3):1519-1536. doi:10.17341/gazimmfd.494086
Chicago Öztürk, Müslüm, ve Turan Paksoy. “Tedarikçi seçimi için Yeni Bir aralık Tip-2 Hibrit bulanık Kural Tabanlı AHP Sistemi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35, sy. 3 (Nisan 2020): 1519-36. https://doi.org/10.17341/gazimmfd.494086.
EndNote Öztürk M, Paksoy T (01 Nisan 2020) Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35 3 1519–1536.
IEEE M. Öztürk ve T. Paksoy, “Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi”, GUMMFD, c. 35, sy. 3, ss. 1519–1536, 2020, doi: 10.17341/gazimmfd.494086.
ISNAD Öztürk, Müslüm - Paksoy, Turan. “Tedarikçi seçimi için Yeni Bir aralık Tip-2 Hibrit bulanık Kural Tabanlı AHP Sistemi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35/3 (Nisan 2020), 1519-1536. https://doi.org/10.17341/gazimmfd.494086.
JAMA Öztürk M, Paksoy T. Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi. GUMMFD. 2020;35:1519–1536.
MLA Öztürk, Müslüm ve Turan Paksoy. “Tedarikçi seçimi için Yeni Bir aralık Tip-2 Hibrit bulanık Kural Tabanlı AHP Sistemi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 35, sy. 3, 2020, ss. 1519-36, doi:10.17341/gazimmfd.494086.
Vancouver Öztürk M, Paksoy T. Tedarikçi seçimi için yeni bir aralık tip-2 hibrit bulanık kural tabanlı AHP sistemi. GUMMFD. 2020;35(3):1519-36.

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