A Hybrıd Approach based on Interval Type-2 Fuzzy MCDM for Warehouse Site Selection

Öz

For most companies that aim at delivering the best possible quality product from the right place to the right place, choosing the right warehouse location is very important for operational success. It is of great importance to make a right and future-oriented decision about a new warehouse location, as companies' decision to make the right position is becoming more and more ambiguous in the global business environment. Therefore, choosing the right warehouse location is one of the main problems in the development of a successful supply chain system. In this study, it is aimed to present the multi-criteria decision making (MCDM) method, which is expanded by the interval type-2 fuzzy sets to select the appropriate warehouse location. The proposed approach includes the AHP and TOPSIS methods under the interval type-2 fuzzy environment to overcome the uncertainty in the judgment and statements of the experts in decision-making. Fuzzy AHP is used to determine the weight of criteria determined by experts in the selection of warehouses; then the TOPSIS method is used to obtain the ranking of alternative depots with spacing type-2 trapezoidal fuzzy numbers. To show the proposed application of the methodology, a study is carried out using real data for companies wishing to open a new store in Turkey.

Anahtar Kelimeler

• Interval Type-2 Fuzzy AHP,Interval Type-2 Fuzzy TOPSIS,Warehouse Site Selection Atıf

Depo Yeri Seçimi için Aralık Tip-2 Bulanık ÇKKV Tabanlı Hibrit Bir Yaklaşım

Öz

Doğru yerden doğru yere mümkün olan en iyi kalitede ürün teslim etmeyi amaçlayan çoğu şirket için, doğru depo yeri seçimi stratejik başarı için çok önemlidir. Şirketlerin doğru konum kararını vermesi küresel iş ortamında her geçen gün belirsiz hale geldiğinden, yeni bir depo yeri hakkında sağlam ve geleceğe dönük bir karar vermek büyük önem taşımaktadır. Bu nedenle, doğru depo yeri seçiminin belirlenmesi, başarılı bir tedarik zinciri sisteminin geliştirilmesindeki temel problemlerden biri olmuştur. Bu çalışmada, uygun depo yerini seçmek için aralık tip-2 bulanık kümeleriyle genişletilen çok kriterli karar verme (ÇKKV) yönteminin sunulması amaçlanmaktadır. Önerilen yaklaşım, karar vermede uzmanların yargılarında ve ifadelerinde yer alan belirsizliğinin üstesinden gelmek için aralık tip-2 bulanık ortam altında AHP ve TOPSIS yöntemlerini içermektedir. Bulanık AHP, depo seçiminde uzmanlarca belirlenen kriterlerin ağırlıklarını belirlemek için kullanılmış; ardından aralık tip-2 yamuksal bulanık sayılar ile alternatif depoların sıralamasını elde etmek için TOPSIS yöntemi kullanılmıştır. Önerilen metodolojinin uygulamasını göstermek için, Türkiye’de yeni bir depo açmak isteyen firma için gerçek verileri kullanarak bir çalışma yürütülmüştür.

Anahtar Kelimeler

• Aralık Tip-2 Bulanık AHP,Aralık Tip-2 Bulanık TOPSIS,Depo Yeri Seçimi

Kaynakça

1. Aktepe, A., & Ersöz, S. (2014). AHP-VIKOR ve MOORA Yöntemlerinin Depo Yeri Seçim Probleminde Uygulanması. Endüstri Mühendisliği Dergisi, 25(1–2), 2–15.
2. Ashrafzadeh, M., Mokhatab rafiei, F., Mollaverdi, N., & Zare, Z. (2012). Application of fuzzy TOPSIS method for the selection of Warehouse Location: A Case Study. Interdiscipl J Contemp Res Business (C. 3).
3. Asır, Ö., & Erol, E. (2016). COPRAS ve MOORA Yöntemlerinin Depo Yeri Seçim Problemine Uygulanması. Ekonomi, Isletme, Siyaset ve Uluslararası Iliskiler Dergisi (JEBPIR), 2(1), 23–42.
4. Baykasoğlu, A., & Gölcük, İ. (2017). Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Systems with Applications, 70, 37–51. https://doi.org/10.1016/J.ESWA.2016.11.001
5. Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9
6. Büyüközkan, G., & Uztürk, D. (2017). Combined QFD TOPSIS approach with 2-tuple linguistic information for warehouse selection. Içinde 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (ss. 1–6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015684
7. Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., & Baracli, H. (2013). An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review, 58, 28–51. https://doi.org/10.1016/J.TRE.2013.06.006
8. Chai, J., Liu, J. N. K., & Xu, Z. (2013). A rule-based group decision model for warehouse evaluation under interval-valued Intuitionistic fuzzy environments. Expert Systems with Applications, 40(6), 1959–1970. https://doi.org/10.1016/J.ESWA.2012.10.003

9. Chen, C.-T. (2001). A fuzzy approach to select the location of the distribution center. Fuzzy Sets and Systems, 118(1), 65–73. https://doi.org/10.1016/S0165-0114(98)00459-X
10. Chen, S. M., & Lee, L. W. (2010a). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, 37(4), 2790–2798. https://doi.org/10.1016/j.eswa.2009.09.012
11. Chen, S. M., & Lee, L. W. (2010b). Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Systems with Applications, 37(1), 824–833. https://doi.org/10.1016/j.eswa.2009.06.094
12. Demirel, T., Demirel, N. Ç., & Kahraman, C. (2010). Multi-criteria warehouse location selection using Choquet integral. Expert Systems with Applications, 37(5), 3943–3952. https://doi.org/10.1016/J.ESWA.2009.11.022
13. Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2013). A hybrid fuzzy technique for the selection of warehouse location in a supply chain under a utopian environment. International Journal of Management Science and Engineering Management, 8(4), 250–261. https://doi.org/10.1080/17509653.2013.825075
14. Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016). Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria. International Journal of Management Science and Engineering Management, 11(4), 262–278. https://doi.org/10.1080/17509653.2015.1086964
15. Erdoğan, M., & Kaya, İ. (2016). A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Applied Soft Computing, 39, 84–93. https://doi.org/10.1016/J.ASOC.2015.11.013
16. Frazelle, E. (2015). Supply chain strategy. McGraw Hill.
17. Huang, S., Wang, Q., Batta, R., & Nagi, R. (2015). An integrated model for site selection and space determination of warehouses. Computers & Operations Research, 62, 169–176. https://doi.org/10.1016/J.COR.2014.10.015
18. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications. Springer-Verlag. Tarihinde adresinden erişildi https://books.google.com.tr/books?id=X-wYAQAAIAAJ
19. Jha, M., Raut, R., B. Gardas, B., & Raut, V. (2018). A sustainable warehouse selection: An interpretive structural modelling approach. International Journal of Procurement Management (C. 11). https://doi.org/10.1504/IJPM.2018.090025
20. Kabak, M., & Keskin, İ. (2018). Hazardous Materials Warehouse Selection Based on GIS and MCDM. Arabian Journal for Science and Engineering, 43(6), 3269–3278. https://doi.org/10.1007/s13369-018-3063-z
21. Kahraman, C., Öztayşi, B., Uçal Sarı, İ., & Turanoğlu, E. (2014). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59, 48–57. https://doi.org/10.1016/J.KNOSYS.2014.02.001
22. Karmaker, C., & Saha, M. (2015). Optimization of warehouse location through fuzzy multi-criteria decision making methods. Decision Science Letters (C. 4). https://doi.org/10.5267/j.dsl.2015.4.005
23. Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773–9779. https://doi.org/10.1016/J.ESWA.2011.02.022
24. Vlachopoulou, M., Silleos, G., & Manthou, V. (2001). Geographic information systems in warehouse site selection decisions. International Journal of Production Economics, 71(1–3), 205–212. https://doi.org/10.1016/S0925-5273(00)00119-5