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

Year 2020, Volume: 8 Issue: İktisadi ve İdari Bilimler, 283 - 294, 18.12.2020

Ali Aygün Yürüyen , Alptekin Ulutaş

https://doi.org/10.18506/anemon.767354

Cited By: 6

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.

Keywords

Fuzzy AHP, Fuzzy EDAS, Multiple Criteria Decision Making, Third Party Logistics

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

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.

Keywords

Bulanık AHP, Bulanık EDAS, Çok Kriterli Karar Verme, Üçüncü Parti Lojistik

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.
• Lehmusvaara, A., Tuominen, M., & Korpela, J. (1999). An integrated approach for truck carrier selection. International Journal of Logistics: Research and Applications, 2(1), 5-20.
• Li, D. F., & Wan, S. P. (2014). Fuzzy heterogeneous multiattribute decision making method for outsourcing provider selection. Expert systems with applications, 41(6), 3047-3059.
• Li, F., Li, L., Jin, C., Wang, R., Wang, H., & Yang, L. (2012). A 3PL supplier selection model based on fuzzy sets. Computers & Operations Research, 39(8), 1879-1884.
• Liu, H. T., & Wang, W. K. (2009). An integrated fuzzy approach for provider evaluation and selection in third-party logistics. Expert Systems with Applications, 36(3), 4387-4398.
• Li, W., Yu, S., Pei, H., Zhao, C., & Tian, B. (2017). A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality. Journal of Air Transport Management, 60, 49-64.
• Mangla, S. K., Kumar, P., & Barua, M. K. (2015). Risk analysis in green supply chain using fuzzy AHP approach: A case study. Resources, Conservation and Recycling, 104, 375-390.
• McGinnis, M. A., Kochunny, C. M., & Ackerman, K. B. (1995). Third party logistics choice. The International Journal of Logistics Management. 6(2): 93-102. Menon, M. K., McGinnis, M. A., & Ackerman, K. B. (1998). Selection criteria for providers of third-party logistics services: an exploratory study. Journal of business logistics, 19(1), 121-137.
• Mukul, E., Büyüközkan, G., & Güler, M. (2019). Strategic analysis of intelligent transportation systems. Beykoz Akademi Dergisi, Özel Sayı,148-158.
• Özbek, A., & Eren, T. (2012). Üçüncü Parti Lojistik (3PL) Firmanın Analitik Hiyerarşi Süreciyle (AHS) Belirlenmesi. International Journal of Engineering Research and Development, 4(2), 46-54.
• Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383-407.
• Peng, J. (2012). Selection of logistics outsourcing service suppliers based on AHP. Energy Procedia, 17, 595-601.
• Perçin, S., & Min, H. (2013). A hybrid quality function deployment and fuzzy decision-making methodology for the optimal selection of third-party logistics service providers. International Journal of Logistics Research and Applications, 16(5), 380-397.
• Perçin, S. (2019). An integrated fuzzy SWARA and fuzzy AD approach for outsourcing provider selection. Journal of Manufacturing Technology Management. 30(2). 531-552.
• Polat, G., & Bayhan, H. G. (2020). Selection of HVAC-AHU system supplier with environmental considerations using Fuzzy EDAS method. International Journal of Construction Management, Yayın Aşamasında, 1-9.
• Raut, R., Kharat, M., Kamble, S., & Kumar, C. S. (2018). Sustainable evaluation and selection of potential third-party logistics (3PL) providers. Benchmarking: An International Journal, 25(1), 76-97.
• Saaty, T. L. (1980). "The Analytic Hierarchy Process". New York: McGraw Hill.
• Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2014). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications, 41(1), 50-58.
• Sevim, Ş., Akdemir, A., & Vatansever, K. (2008). Lojistik Faaliyetlerinde Dış Kaynak Kullanan İşletmelerin Aldıkları Hizmetlerin Kalitesinin Değerlendirilmesine Yönelik Bir İnceleme. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(1), 1-27.
• Sharma K.S. & Kumar V. (2015). Optimal Selection of ThirdParty Logistics Service Providers Using Quality Function Deployment and Taguchi Loss Function. Benchmarking: An International Journal, 22(7), 1281-1300.
• 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.
• Singh, A., & Prasher, A. (2019). Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence, 30(3-4), 284-300.
• Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a third-party logistics (3PL) provider using a rough SWARA–WASPAS model based on a new rough dombi aggregator. Symmetry, 10(8), 305.
• Stanujkic, D., Zavadskas, E. K., Ghorabaee, M. K., & Turskis, Z. (2017). An extension of the EDAS method based on the use of interval grey numbers. Studies in Informatics and Control, 26(1), 5-12.
• Stević, Ž., Vasiljević, M., Puška, A., Tanackov, I., Junevičius, R., & Vesković, S. (2019). Evaluation of suppliers under uncertainty: a multiphase approach based on fuzzy AHP and fuzzy EDAS. Transport, 34(1), 52-66.
• Sudrajat, H. A., Paramartha, D. G. A., & Purba, H. H. (2019). Third-Party Logistics Company Supplier Evaluation using Analytical Hierarchy Process Method: A Case Study in the Manufacturing Industry. International Journal of Advances in Scientific Research and Engineering, 5(2), 28-35.
• Zhou, T., Chen, J., & Qiao, Z. (2003). The Competition Ability Index System and Vague Evaluation of Third-Party Logistics Corporation. Logistics Management, 26 (5), 30-32.
• Thakkar, J., Deshmukh, S. G., Gupta, A. D., & Shankar, R. (2005). Selection of third-party logistics (3PL): a hybrid approach using interpretive structural modeling (ISM) and analytic network process (ANP). Supply Chain Forum: An International Journal, 6(1), 32-46.
• Ulutaş, A., Özkan, A. M., & Tağraf, H. (2018). Bulanık Analitik Hiyerarşi Süreci ve Bulanık Gri İlişkisel Analizi Yöntemleri Kullanılarak Personel Seçimi Yapılması. Elektronik Sosyal Bilimler Dergisi, 17(65), 223-232.
• Ulutas, A. (2020). An Integrated Rough Model for Third Party Logistics Service Provider Selection. In Theoretical and Applied Mathematics in International Business (pp. 91-106). IGI Global.
• Xu, L., Liu, H., Wang, S., & Wang, K. (2009). Modelling and analysis techniques for cross‐organizational workflow systems. Systems Research and Behavioral Science: The Official Journal of the International Federation for Systems Research, 26(3), 367-389.
• Vaidyanathan, G. (2005). A framework for evaluating third-party logistics. Communications of the ACM, 48(1), 89-94.
• Vatansever, K. & Uluköy, M. (2013). Kurumsal kaynak planlaması sistemlerinin bulanık AHP ve bulanık MOORA yöntemleriyle seçimi: Üretim sektöründe bir uygulama. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 11(2), 274-293.
• Vesković, S., Stević, Ž., Karabašević, D., Rajilić, S., Milinković, S., & Stojić, G. (2020). A New Integrated Fuzzy Approach to Selecting the Best Solution for Business Balance of Passenger Rail Operator: Fuzzy PIPRECIA-Fuzzy EDAS Model. Symmetry, 12(5), 743.
• Wang, B., Song, J., Ren, J., Li, K., & Duan, H. (2019). Selecting sustainable energy conversion technologies for agricultural residues: A fuzzy AHP-VIKOR based prioritization from life cycle perspective. Resources, Conservation and Recycling, 142, 78-87.
• Wang, J. J., Wang, M. M., Liu, F., & Chen, H. (2015). Multistakeholder strategic third-party logistics provider selection: a real case in China. Transportation Journal, 54(3), 312-338.
• Yayla, A. Y., Oztekin, A., Gumus, A. T., & Gunasekaran, A. (2015). A hybrid data analytic methodology for 3PL transportation provider evaluation using fuzzy multi-criteria decision making. International Journal of Production Research, 53(20), 6097-6113.
• Yildirim, B. F., & Mercangoz, B. A. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45.
• Zhang, H., Li, X., Liu, W., Li, B., & Zhang, Z. (2004, October). An application of the AHP in 3PL vendor selection of a 4PL system. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583) (Vol. 2, pp. 1255-1260). IEEE.
• Zhang, G., Shang, J., & Li, W. (2012). An information granulation entropy-based model for third-party logistics providers evaluation. International Journal of Production Research, 50(1), 177-190.
• Zolfani, S. H., Sedaghat, M., & Zavadskas, E. K. (2012). Performance evaluating of rural ICT centers (telecenters), applying fuzzy AHP, SAW-G and TOPSIS Grey, a case study in Iran. Technological and Economic Development of Economy, 18(2), 364-387.