SUPPLIER SELECTION WITH INTERVAL TYPE-2 FUZZY RULE-BASED AHP APPROACH

Year 2019, Issue: 3, 115 - 141, 15.06.2019

Turan Paksoy , Müslüm Öztürk

Abstract

The use of type-2 fuzzy logic sets is more
flexible and successful than the classical logic sets in solving problems where
the criteria and alternatives are large and the uncertainty is high. For this
reason, integrating MCDM (multi criteria decision-making) 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. The ranking of
three alternatives under three criteria for the selection of appropriate
suppliers is used in this study on account of expert opinions. Primarily, the
interval fuzzy type-2 Analytical Hierarchy Process method, which has no rule
base and recommended by Kahraman et al. (2014) is used.  Then the alternatives are ranked by the
Interval Type-2 Fuzzy-Rule Based Analytical Hierarchy Process as recommended in
the context of this study. The ranking performance evaluation based on the
Kendall Tau correlation is made for the ranking results that were obtained by
the two methods. It is seen that the ranking performance with Interval Type-2
Fuzzy-Rule Based Analytical Hierarchy Process is higher. Moreover, application,
it is revealed that the applied interval fuzzy type-2 Analytical Hierarchy
Process method can be used in the selection of suppliers for the firms.

Keywords

Interval Type-2 Fuzzy Logic, Supply Chain Management, Supplier Selection, Interval Type-2 Fuzzy Rule Based AHP

ARALIK TİP-2 BULANIK KURAL TABANLI AHP YAKLAŞIMI İLE TEDARİKÇİ SEÇİMİ

Abstract

Kriterlerin ve alternatiflerin çok ve belirsizliğin
yoğun olduğu problemlerin çözümünde klasik mantık kümelerine göre, tip-2
bulanık mantık kümeleri kullanılır. Çünkü daha esnek ve başarılıdırlar. Bu nedenle,
Çok Kriterli Karar Verme problemlerinin aralık 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. Bu çalışmada, uzman görüşleri de dikkate alınarak
uygun tedarikçi seçimi için üç kriter altında üç alternatifin sıralanması; öncelikle
Kahraman ve ark. (2014) tarafından önerilen kural tabanı olmayan Aralık Tip-2 Bulanık
Analitik Hiyerarşi Prosesi Yöntemi kullanılmış, daha sonra ise alternatiflerin
sıralanması bu çalışma kapsamında önerilen Aralık Tip-2 Bulanık-Kural Tabanlı
Analitik Hiyerarşi Prosesi ile yapılmıştır. İki yöntem sonucunda elde edilen
sıralama sonuçları üzerinde Kendall Tau Korelasyonuna
dayalı Sıralama Performansı Değerlendirmesi işlemi yapılmış ve elde edilen
sonuçlardan Aralık Tip-2 Bulanık-Kural
Tabanlı Analitik Hiyerarşi Prosesi ile yapılan sıralama performansının daha
yüksek olduğu görülmüştür. Ayrıca yapılan uygulama çalışması sonucunda,
uygulanan Aralık Tip-2 Bulanık Analitik Hiyerarşi Prosesi Yönteminin, firmalar
için tedarikçi seçiminde kullanılabileceği ortaya konulmuştur. 

Keywords

Aralık Tip 2 Bulanık Mantık, Tedarik Zinciri Yönetimi, Tedarikçi Seçimi, Aralık Tip-2 Bulanık Kural Tabanlı AHP

References

• • 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.
• • CHAN, F. T. & KUMAR, N., (2007), Global supplier development considering risk factors using fuzzy extended AHP-based approach, Omega, 35 (4), 417-431.
• • 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.
• • ÇALIK, A. ve PAKSOY, T., (2017), Aralık Tip-2 Bulanık AHP Yöntemi İle Üçüncü Parti Tersine Lojistik (3PTL) Firma Seçimi, Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 20 (1), 52-67.
• • DI MARTINO, F. & SESSA, S., (2014), Type-2 interval fuzzy rule-based systems in spatial analysis, Information Sciences, 279, 199-212.
• • 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.
• • HAMDAN, S. & CHEAITOU, A., (2017), Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach, Computers & Operations Research, 81, 282-304.
• • BÜYÜKÖZKAN, G., KAHRAMAN, C., & RUAN, D., (2004), A fuzzy multi-criteria decision approach for software development strategy selection, International Journal of General Systems, 33 (2-3), 259-280.
• • KAHRAMAN, C., ÖZTAYŞİ, B., SARI, İ. U. & TURANOĞLU, E., (2014), Fuzzy analytic hierarchy process with interval type-2 fuzzy sets, Knowledge-Based Systems, 59, 48-57.
• • 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.
• • KARAKAŞOĞLU, N., (2008), Bulanık çok kriterli karar verme yöntemleri ve uygulama (Master's thesis).
• • KARAKÖSE, M. ve Akın, E., (2013), Tip-2 Bulanık Filtre, Elektrik-eletronik-Bilgisayar Mühendisliği 10. Ulusal Kongresi, İstanbul.
• • KARNIK, N. N., (1998), Type-2 fuzzy logic systems, Ph.D. Dissertation, ProQuest Dissertations & Theses (PQDT), De Montfort University, UK.
• • GHORABAEE, M. K., ZAVADSKAS, E. K., AMIRI, M. & ESMAEILI, A., (2016), Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets, Journal of Cleaner Production, 137, 213-229.
• • KILIC, H. S., (2013), An integrated approach for supplier selection in multi-item/multi-supplier environment, Applied Mathematical Modelling, 37 (14-15), 7752-7763.
• • LIAO, H., Xu, Z. & Zeng, X. J., (2015), Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making, IEEE Transactions on Fuzzy Systems, 23 (5), 1343-1355.
• • OMURCA, S. I., (2013), An intelligent supplier evaluation, selection and development system, Applied Soft Computing, 13 (1), 690-697.
• • 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.
• • QIN, J., LIU, X. & PEDRYCZ, W., (2017), An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment, European Journal of Operational Research, 258 (2), 626-638.
• • 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 Systems with Applications, 41 (18), 8165-8179.
• • 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.
• • 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.
• • 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.
• • THOMAS, D. J. & GRIFFIN, P. M., (1996), Coordinated supply chain management, European journal of operational research, 94 (1), 1-15.
• • TÜRK, S., JOHN, R. & ÖZCAN, E., (2014), Interval type-2 fuzzy sets in supplier selection, In Computational Intelligence (UKCI), 2014 14th UK Workshop on (pp. 1-7), IEEE.
• • ULU, C., (2013), Granüler tip-2 bulanık yapılar kullanılarak sistemlerin modellenmesi ve kontrolü, (Doctoral dissertation, Fen Bilimleri Enstitüsü).
• • YAAKOB, A. M., KHALIF, K. M. N. K., GEGOV, A. & RAHMAN, S. F. A., (2015), Interval type 2-fuzzy rule based system approach for selection of alternatives using TOPSIS, In Computational Intelligence (IJCCI), 2015 7th International Joint Conference on (Vol. 2, pp. 112-120), IEEE.
• • 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.
• • 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.
• • ZADEH, L. A., (1975), Calculus of fuzzy restrictions, In Fuzzy sets and their applications to cognitive and decision processes (pp. 1-39).