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Selection Of The Third Party Logistics Company With Fuzzy AHP And Fuzzy EDAS Methods

Year 2020, , 283 - 294, 18.12.2020
https://doi.org/10.18506/anemon.767354

Abstract

Businesses need to work with the right and appropriate 3PL (third party logistics) companies to gain competitive advantage and increase their profit margins. Therefore, choosing the right and the appropriate 3PL firm is important for businesses. More than one criteria should be taken into consideration for the selection of a 3PL company. Multi-criteria decision making (MCDM) methods can be used in the 3PL selection problem due to considering more than one criteria. In this study, a fuzzy MCDD model consisting of Fuzzy AHP and Fuzzy EDAS methods has been developed and the application of the developed model was made in military equipment producing factory in Ankara. While the Fuzzy AHP method was used to find the criteria weights, the Fuzzy EDAS method was used to determine the best 3PL firm.

References

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Bulanık AHP ve Bulanık EDAS Yöntemleri İle Üçüncü Parti Lojistik Firması Seçimi

Year 2020, , 283 - 294, 18.12.2020
https://doi.org/10.18506/anemon.767354

Abstract

İşletmeler, rekabet avantajı elde etmek ve kar marjlarını artırmak için doğru ve uygun 3PL (üçüncü parti lojistik) firmaları ile çalışmaları gerekmektedir. Bu yüzden doğru ve uygun 3PL firması seçimi işletmeler için önemlidir. 3PL firması seçimi için birden fazla kriter dikkate alınmalıdır. Birden fazla kriter göz önünde bulundurulmasından dolayı çok kriterli karar verme (ÇKKV) yöntemleri 3PL seçimi probleminde kullanılabilir. Bu çalışmada Bulanık AHP ve Bulanık EDAS yöntemlerinden oluşan bir bulanık ÇKKV modeli geliştirilmiştir ve geliştirilen modelin uygulaması Ankara’da bulunan bir askeri araç-gereç üreten fabrikada yapılmıştır. Kriter ağırlıklarının bulunmasında Bulanık AHP yöntemi kullanılırken, en uygun 3PL firmanın belirlenmesi için Bulanık EDAS yöntemi kullanılmıştır.

References

  • Akman, G., & Baynal, K. (2014). Logistics service provider selection through an integrated fuzzy multicriteria decision making approach. Journal of Industrial Engineering, 2014.1-16.
  • Alkhatib, S. F., Darlington, R., Yang, Z., & Nguyen, T. T. (2015). A novel technique for evaluating and selecting logistics service providers based on the logistics resource view. Expert systems with applications, 42(20), 6976-6989.
  • Altan, Ş., & Aydın, E. K. (2015). Bulanık DEMATEL ve Bulanık TOPSIS Yöntemleri ile Üçüncü Parti Lojistik Firma Seçimi için Bütünleşik Bir Model Yaklaşımı. Süleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 20(3), 99-119.
  • Ashenbaum, B., Maltz, A., & Rabinovich, E. (2005). Studies of Trends in Third-party Logistics Usage: What Can We Conclude?. Transportation Journal, 44(3), 39-50.
  • Asian, S., Pool, J. K., Nazarpour, A., & Tabaeeian, R. A. (2019). On the importance of service performance and customer satisfaction in third-party logistics selection. Benchmarking: An International Journal, 26(5), 1550-1564.
  • Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195, 106-117.
  • Bali, Ö., Tutun, S., Pala, A., & Çörekçi, C. (2014). A MCDM Approach with Fuzzy DEMATEL and Fuzzy TOPSIS For 3 PL Provider Selection. Journal of Engineering and Natural Sciences, 32, 222-239.
  • Bayrakdaroğlu F.K. & Kundakcı N. (2019). Bulanık EDAS Yöntemi ile Ar-Ge Projesi Seçimi. Uluslararası İktisadi ve İdari İncelemeler Dergisi. (24), 151-170.
  • Bianchini, A. (2018). 3PL provider selection by AHP and TOPSIS methodology. Benchmarking: An International Journal, 25(1), 235-252.
  • Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal. 11(4), 294-308.
  • Buckley, J. J. (1985). Fuzzy Hierarchical Analysis. Fuzzy sets and systems, 17(3), 233-247.
  • Büyüközkan, G., Feyzioğlu, O., & Nebol, E. (2008). Selection of the strategic alliance partner in logistics value chain. International Journal of Production Economics, 113(1), 148-158.
  • Demircan, M. L., & Tunc, S. (2019, July). A proposed service level improvement methodology for public transportation using Interval Type-2 Fuzzy EDAS based on customer satisfaction data. In International Conference on Intelligent and Fuzzy Systems (pp. 1351-1359). Springer, Cham.
  • Dožić, S., Lutovac, T., & Kalić, M. (2018). Fuzzy AHP approach to passenger aircraft type selection. Journal of Air Transport Management, 68, 165-175.
  • Ecer, F. (2015). Performance evaluation of internet banking branches via a hybrid MCDM model under fuzzy environment. Economic Computation & Economic Cybernetics Studies & Research, 49(2). 211-230.
  • Ecer, F. (2018). Third-party logistics (3PLs) provider selection via Fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615-634.
  • Falsini, D., Fondi, F., & Schiraldi, M. M. (2012). A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration. International Journal of Production Research, 50(17), 4822-4829.
  • Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451.
  • Ghorabaee, M. K., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. International journal of computers communications & control, 11(3), 358-371.
  • Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., & Antuchevičienė, J. (2017a). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66-78.
  • Govindan, K., & Chaudhuri, A. (2016). Interrelationships of risks faced by third party logistics service providers: A DEMATEL based approach. Transportation Research Part E: Logistics and Transportation Review, 90, 177-195.
  • Govindan K., Khodaverdi R. & Vafadarnikjoo A. (2016). A Grey DEMATEL Approach to Develop Third-Party Logistics Provider Selection Criteria. Industrial Management & Data Systems. 116(4), 690-722.
  • Göl, H., & Çatay, B. (2007). Third‐party logistics provider selection: insights from a Turkish automotive company. Supply Chain Management: An International Journal, 12(6), 379-384.
  • Guoyi, X., & Xiaohua, C. (2011, August). Research on the third party logistics supplier selection evaluation based on AHP and entropy. In 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC) (pp. 788-792). IEEE.
  • Gupta, R., Sachdeva, A., & Bhardwaj, A. (2011). A framework for the selection of logistic service provider using fuzzy delphi and fuzzy topsis. In Intelligent Automation and Systems Engineering (pp. 189-202). Springer, New York, NY.
  • Gupta, R., Sachdeva, A., Sharma, V., & Bhardwaj, A. (2012). Selection of logistic service provider using fuzzy PROMETHEE for a cement industry. Journal of Manufacturing Technology Management, 23(7), 899- 921.
  • Hasheminasab, H., Zolfani, S. H., Bitarafan, M., Chatterjee, P., & Ezabadi, A. A. (2019). The Role of Façade Materials in Blast-Resistant Buildings: An Evaluation Based on Fuzzy Delphi and Fuzzy EDAS. Algorithms, 12(6), 119.
  • Heo, E., Kim, J., & Boo, K. J. (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and sustainable energy reviews, 14(8), 2214-2220.
  • Ho, W., He, T., Lee, C. K. M., & Emrouznejad, A. (2012). Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach. Expert Systems with Applications, 39(12), 10841-10850.
  • Hsu, C. C., Liou, J. J., & Chuang, Y. C. (2013). Integrating DANP and modified grey relation theory for the selection of an outsourcing provider. Expert Systems with Applications, 40(6), 2297-2304.
  • Ilieva, G., Yankova, T., & Klisarova-Belcheva, S. (2018). Decision analysis with classic and fuzzy EDAS modifications. Computational and Applied Mathematics, 37(5), 5650-5680.
  • Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications, 29(7), 555-564.
  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274-289.
  • Kahraman, C., Ghorabaee, M.K., Zavadskas, E. K., Cevik Onar, S., Yazdani, M., & Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25(1), 1-12.
  • Karakaşoğlu, N. (2008). Bulanık Çok Kriterli Karar Verme Yöntemleri ve Uygulama. (Yayımlanmış Yüksek Lisans Tezi). Pamukkale Üniversitesi, Sosyal Bilimler Enstitüsü, Denizli. YÖK Ulusal Tez Merkezi veri tabanından elde edildi. (Tez no: 226810)
  • Korucuk, S. (2018). Soğuk zincir taşımacılığı yapan işletmelerde 3PL firma seçimi: İstanbul örneği. Iğdır Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16, 341-365.
  • Lee, A. H., Lin, C. Y., Wang, S. R., & Tu, Y. M. (2010). The construction of a comprehensive model for production strategy evaluation. Fuzzy Optimization and Decision Making, 9(2), 187-217.
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There are 77 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Ali Aygün Yürüyen 0000-0002-0323-7789

Alptekin Ulutaş 0000-0002-8130-1301

Publication Date December 18, 2020
Acceptance Date December 2, 2020
Published in Issue Year 2020

Cite

APA Yürüyen, A. A., & Ulutaş, A. (2020). Bulanık AHP ve Bulanık EDAS Yöntemleri İle Üçüncü Parti Lojistik Firması Seçimi. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 8(İktisadi ve İdari Bilimler), 283-294. https://doi.org/10.18506/anemon.767354

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.