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

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. 

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

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.

___

  • • 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).