BULANIK TOPSIS VE BULANIK VIKOR YÖNTEMLERİ İLE EN UYGUN TEDARİKÇİ SEÇİMİ ÜZERİNE BİR UYGULAMA

Year 2021, Volume: 23 Issue: 2, 627 - 640, 30.06.2021

Muhammet Enes Akpınar

https://doi.org/10.16953/deusosbil.843914

Abstract

Günümüzün her geçen gün artmaya devam eden rekabet durumunda işletmelerin sürdürülebilir bir şekilde faaliyetlerine devam edebilmeleri için belirli stratejilere sahip olmalıdır. Bu stratejilerin başında da hiç kuşkusuz tedarikçiler ile olan stratejik ilişkiler yer almaktadır. Tedarikçiler ile yaşanabilecek herhangi bir problem işletmenin üretiminde ciddi zorluklara sebep olacaktır. Dolayısıyla işletmeler çalıştıkları tedarikçilerini en iyi şekilde belirlemeli ve uzun vadeli tedarikçi ilişkileri kurmalıdır. Bu çalışmada da gıda sektöründe faaliyet gösteren bir firmada tedarikçi seçimi üzerine bir çalışma yapılmıştır. Çalışmada satın alma departmanı personellerinin belirlediği beş farklı tedarikçi beş farklı kriter üzerinden değerlendirilmiştir. Değerlendirme için karar vericilerin daha esnek karar vermelerine imkan tanıyan bulanık TOPSIS yöntemi kullanılmıştır. Çok kriterli karar verme metotlarında bulanık TOPSIS metodu ile çözülen problemin sonuçları kıyaslama açısından bulanık VIKOR ile de çözülmüş ve sonuçlar kıyaslanmıştır.

Keywords

Çok Kriterli Karar Verme, , Tedarikçi Seçimi, Bulanık VIKOR, Bulanık TOPSIS

AN APPLICATION ON THE MOST SUITABLE SUPPLIER SELECTION WITH FUZZY TOPSIS AND FUZZY VIKOR METHODS

Abstract

In today's increasing competitive situations companies need dynamic strategies to survive. Strategic relations with suppliers are undoubtedly at the top of these strategies. Any problems that may occur with the suppliers will cause serious difficulties during the production process of the company. Therefore, companies should determine their suppliers in the best way and establish long-term relationships. In this study, an application was conducted on supplier selection in a food company considering multiple criteria decision-making methodology. To achieve this aim, five different suppliers were determined by the purchasing department managers and they were evaluated concerning five different criteria. Fuzzy TOPSIS methodology was proposed to decide the best supplier. The problem is solved by fuzzy TOPSIS methodology. Finally, the same problem is also solved by using fuzzy VIKOR methodology to compare results.

Keywords

Multi Criteria Decision-Making, Fuzzy VIKOR, Supplier Selection, Fuzzy TOPSIS

References

• Akpınar, M. E. (2016). Vikor tabanlı yeni bir çok kriterli sınıflandırma metodu: Vikorsort, (Published Master's thesis). Pamukkale University, Graduate School of Natural and Applied Sciences, Denizli.
• Alikhani, R., Torabi, S. A., & Altay, N. (2019). Strategic supplier selection under sustainability and risk criteria. International Journal of Production Economics, 208, 69-82.
• Araz, C. & Ozkarahan, I. (2007). Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure. International journal of production economics, 106(2), 585-606.
• Chen, C. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9.
• Chen, S. J. & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making, 289-486.
• Chen, C. Lin, C. & Huang, S. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economies, 102(2), 289-301.
• Chu, T. C. & Lin Y. C. (2003). A Fuzzy TOPSIS Method For Robot Selection. International Journal of Advanced Manufacturing Technology, 21, 284-290.
• Demir, L., Akpınar, M. E., Araz, C. & Ilgın. M. A. (2018). A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT, Expert Systems with Applications, 114(1), 479-487.
• Deli, I. (2020). A TOPSIS method by using generalized trapezoidal hesitant fuzzy numbers and application to a robot selection problem. Journal of Intelligent & Fuzzy Systems, 38(1), 779-793.
• Dickson, G. W. (1966). An Analysis of Vendor Selection: Systems and Decisions, Journal of Purchasing, 2(1), 5-17.
• Hamdan, S., & Cheaitou, A. (2017). Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability. Computers & Industrial Engineering, 110, 573-589.
• Hwang, C. L. & Yoon, K. (1981). Multiple attribute decision making: methods and applications, Springer-Verlag, Berlin/Hiedelberg, 2, 16.
• Jahanshahloo, G. R., Lotfi, F.H. & Izadikhah, M. (2006). Extension of the TOPSIS Method for Decision-Making Problems with Fuzzy Data, Applied Mathematics and Computation, 181, 1544-1551.
• Kannan, D., Mina, H., Nosrati-Abarghooee, S., & Khosrojerdi, G. (2020). Sustainable circular supplier selection: A novel hybrid approach. Science of the Total Environment, 722, 137936.
• Lei, F., Wei, G., Gao, H., Wu, J., & Wei, C. (2020). TOPSIS method for developing supplier selection with probabilistic linguistic information. International Journal of Fuzzy Systems, 1-11.
• Lo, H. W., Liou, J. J., Wang, H. S., & Tsai, Y. S. (2018). An integrated model for solving problems in green supplier selection and order allocation. Journal of cleaner production, 190, 339-352.
• Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
• Pan, A. C. (1989). Allocation of order quantity among suppliers, Journal of Purchasing and Materials Management, 25(3), 36-39.
• Rashidi, K., & Cullinane, K. (2019). A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Systems with Applications, 121, 266-281.
• Singh, R. K., Gunasekaran, A., & Kumar, P. (2018). Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach. Annals of Operations Research, 267(1), 531-553.
• Silalahi, A., Sukwadi, R., Pramesjwari, D. A. H., Wahyu, C., & Oktavia, C. N. (2019). Integrated analytic hierarchy process and mixed integer programming for supplier selection in mold and dies industry. Jurnal Sistem dan Manajemen Industri, 3(2), 124-133.
• Tam M. C. Y & Tummala V. M. R. (2001). An Application of The AHP in Vendor Selection of a Telecommunications System, OMEGA, 29(2), 171-182.
• Tirkolaee, E. B., Mardani, A., Dashtian, Z., Soltani, M., & Weber, G. W. (2020). A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. Journal of Cleaner Production, 250, 119517.
• Zeng, S., Chen, S. M., & Fan, K. Y. (2020). Interval-valued intuitionistic fuzzy multiple attribute decision making based on nonlinear programming methodology and TOPSIS method. Information Sciences, 506, 424-442.
• Zimmermann, H. J. (1987). Fuzzy sets, decision making, and expert systems. Boston: Kluwer Academic Publishers.
• Tsaur S. H., Chang T. Y. & Yen C. H. (2002). The evaluation of airline service quality by fuzzy MCDM, Tourism Management, 23, 107-115
• Wang Y. M. & Elhag, T. M. S. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment, Expert Systems With Applications, 31, 309-319
• Weber, C. A., Current, J. R. & Benton, C. (1991). Vendor selection criteria and methods, European Journal of Operational Research, 50(1), 2-18.
• Weber, C. A., Current, J. & Desai, A. (2000). An optimization approach to determining the number of vendors to employ, Supply Chain Management: An International Journal, 5(2), 90.
• Xu, Z., Qin, J., Liu, J., & Martinez, L. (2019). Sustainable supplier selection based on AHPSort II in interval type-2 fuzzy environment. Information Sciences, 483, 273-293.