Amindoust, A. Ahmed, S. & Saghafinia, A. (2012). Location decision of supply chain management in the auto motive industry. International Journal of Engineering, 1(2), 2305-8269.
Aruldoss, M. Lakshmi, T. M. & Venkatesan, V. P. (2013). A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1(1), 31-43.
Athawale, V. M. & Chakraborty, S. (2010, January). Facility location selection using PROMETHEE II method. In Proceedings of the 2010 international conference on industrial engineering and operations management (pp. 9-10). Bangladesh Dhaka.
Bhatia, M. S. Dora, M. & Jakhar, S. K. (2019). Appropriate location for remanufacturing plant towards sustainable supply chain. Annals of Operations Research, 1-22.
Bolturk, E. & Kahraman, C. (2018). Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-13.
Boran, F. (2011). An integrated intuitionistic fuzzy multi criteria decision making method for facility location selection. Mathematical and Computational Applications, 16(2), 487-496.
Chang, H. S. & Liao, C. H. (2015). Planning emergency shelter locations based on evacuation behavior. Natural Hazards, 76(3), 1551-1571.
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
Chen, J. Wang, J. Baležentis, T. Zagurskaitė, F. Streimikiene, D. & Makutėnienė, D. (2018). Multicriteria approach towards the sustainable selection of a teahouse location with sensitivity analysis. Sustainability, 10(8), 2926.
Demirel, T. Demirel, N. Ç. & Kahraman, C. (2010). Multi-criteria warehouse location selection using Choquet integral. Expert Systems with Applications, 37(5), 3943-3952.
Deveci, M. Akyurt, I. Z. & Yavuz, S. (2018). A GIS-based interval type-2 fuzzy set for public bread factory site selection. Journal of Enterprise Information Management, 31(6), 820-847.
Ertuğrul, İ. (2011). Fuzzy group decision making for the selection of facility location. Group Decision and Negotiation, 20(6), 725-740.
Ervural, B. C. Zaim, S. Demirel, O. F. Aydin, Z. & Delen, D. (2018). An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renewable and Sustainable Energy Reviews, 82, 1538-1550.
Farahani, R. Z. SteadieSeifi, M. & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689-1709.
Genç, T. & Filipe, J. A. (2016). A fuzzy MCDM approach for choosing a tourism destination in Portugal. International Journal of Business and Systems Research, 10(1), 23-44.
Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of environmental management, 226, 201-216.
Güneşli İ. Gündoğan, M. Şeker Alper. (2017). Facility Location Selection Problem: An Application for Student Selection and Placement Centers. Journal of Turkish Operations Management, 1, 27- 37
Han, H. & Trimi, S. (2018). A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert Systems with Applications, 103, 133-145.
Hatami-Marbini, A. & Kangi, F. (2017). An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Applied Soft Computing, 52, 1084-1097.
Hwang, C.L. and Yoon, K. Multiple attribute decision making methods and applications, Springer-Verlag. New York, 1981.
İnançlı, s. & Konak, A. (2011). Türkiye’de ihracatın ithalata bağımlılığı: otomotiv sektörü. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 6(2), 343-362.
Kheybari, S. Kazemi, M. & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied energy, 242, 612-623.
Kumar, A. Sah, B. Singh, A. R. Deng, Y. He, X. Kumar, P. & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609.
Li, S. & Wei, Z. (2018). A hybrid approach based on the analytic hierarchy process and 2-tuple hybrid ordered weighted averaging for location selection of distribution centers. PloS one, 13(11), e0206966.
Lima-Junior, F. R. & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174, 128-141.
Liu, J. & Wei, Q. (2018). Risk evaluation of electric vehicle charging infrastructure public-private partnership projects in China using fuzzy TOPSIS. Journal of Cleaner Production, 189, 211-222.
Oztaysi, B. Onar, S. Ç. Goztepe, K. & Kahraman, C. (2019). A Multi-Expert Interval-Valued Intuitionistic Fuzzy Location Selection for the Maintenance Facility of Armored Vehicles. Journal of Multiple-Valued Logic & Soft Computing, 32.
Rahman, M. S. Ali, M. I. Hossain, U. & Mondal, T. K. (2018). Facility location selection for plastic manufacturing industry in Bangladesh by using AHP method. International Journal of Research in Industrial Engineering, 7(3), 307-319.
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.
Rath, S. & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25-39.
Rezaei, J. (2015). A systematic review of multi-criteria decision-making applications in reverse logistics. Transportation Research Procedia, 10, 766-776.
Roszkowska, E. & Wachowicz, T. (2015). Application of fuzzy TOPSIS to scoring the negotiation offers in ill-structured negotiation problems. European Journal of Operational Research, 242(3), 920-932.
Rudnik, K. & Kacprzak, D. (2017). Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system. Applied Soft Computing, 52, 1020-1041.
Sang, X. Liu, X. & Qin, J. (2015). An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise. Applied Soft Computing, 30, 190-204.
Sennaroglu, B. & Celebi, G. V. (2018). A military airport location selection by AHP integrated PROMETHEE and VIKOR methods. Transportation Research Part D: Transport and Environment, 59, 160-173.
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(1-2), 531-553.
Sirisawat, P. & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303-318.
Solangi, Y. A. Tan, Q. Mirjat, N. H. & Ali, S. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production,236, 1-14
Stanujkić, D. & Meidutė-Kavaliauskienė, I. (2018). An approach to the production plant location selection based on the use of the Atanassov interval-valued intuitionistic fuzzy sets. Transport, 33(3), 835-842.
Tavakkoli, M. R. Mousavi, S. M. & Heydar, M. (2011). An integrated AHP-VIKOR methodology for plant location selection. IJE Transactions B: Applications, 24(2), 127-137.
Trivedi, A. & Singh, A. (2017). A hybrid multi-objective decision model for emergency shelter location-relocation projects using fuzzy analytic hierarchy process and goal programming approach. International Journal of Project Management, 35(5), 827-840.
Uludağ, A. S. & Doğan, H. (2016). Çok kriterli karar verme yöntemlerinin karşılaştırılmasına odaklı bir hizmet kalitesi uygulaması. Çankırı Karatekin Üniversitesi İİBF Dergisi, 6(2), 17-48.
Yayla, A. Y., Yildiz, A., & Ozbek, A. (2012). Fuzzy TOPSIS method in supplier selection and application in the garment industry. Fibres & Textiles in Eastern Europe, 20, 4(93): 20-23.
Yildiz, A., & Yayla, A. (2017). Application of fuzzy TOPSIS and generalized Choquet integral methods to select the best supplier. Decision Science Letters, 6(2), 137-150.
Yu, S. M., Wang, J., Wang, J. Q., & Li, L. (2018). A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Applied Soft Computing, 67, 741-755.
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© Business & Management Studies: An International Journal, 2019
Assoc. Prof. Dr., Bursa Tehnical University
Assist. Prof. Dr., Bursa Technical University
How to Cite
THE MOST SUITABLE FACTORY LOCATION SELECTION FOR TURKEY'S DOMESTIC AUTOMOBILE WITH FUZZY TOPSIS METHOD
Vol 7 No 4 (2019): BUSINESS & MANAGEMENT STUDIES: AN INTERNATIONAL JOURNAL
Submitted: Aug 5, 2019
Published: Sep 22, 2019
For entrepreneurs, in addition to which sector they operate it is also important suitable selection of facility location. Because the selection of an unsuitable facility location after a wrong decision can cause problems such as high costs and inability to reach enough customers. The facility location problem is a decision problem, which contains many criteria and uncertainty and therefore exhibits a fuzzy behavior. In solving such decision problems, the use of scientific methods such as Multi Criteria Decision Making (MCDM) provides convenience to decision makers. In this study, it is aimed to select most suitable facility location for the domestic automobile production which has a strategic importance for Turkey. For this purpose, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to facilitate decision making in fuzzy environments. Seven alternative location (Kocaeli, Bursa-Gemlik, Sakarya, Konya, Izmir-Aliaga, Adana and Eskisehir) have been evaluated according to five different criteria (economic, geographical location, infrastructure, technical and social characteristics) as a result of literature review. At the end of the study, Bursa-Gemlik, which has the highest closeness coefficient, was determined as the most suitable facility location for domestic automobile.