Araştırma Makalesi
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q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection

Yıl 2021, Cilt: 5 Sayı: 1, 588 - 597, 30.06.2021

Öz

In the global competitive environment, businesses have to choose the most suitable third-party logistics company for logistics activities that are outside their core competencies in order to increase quality and reduce costs. Most decision makers make this choice using fuzzy set-based decision-making methods that reflect the uncertainty of real life. In this study, for the first time in the literature, a q-rung orthopair fuzzy set-based decision-making approach is proposed for third-party logistics provider selection. In the study using the TOPSIS method, 10 criteria and the weights of these criteria were determined for 5 alternative companies, the evaluations of decision makers were aggregated, and the alternatives were ranked.

Kaynakça

  • Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69-78.
  • Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal.
  • 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.
  • Ecer, F. (2018). Third-party logistics (3PLs) provider selection via Fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615–634-615–634.
  • Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54(2), 269-287.
  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2016). A grey DEMATEL approach to develop third-party logistics provider selection criteria. Industrial Management & Data Systems.
  • Göl, H., & Çatay, B. (2007). Third‐party logistics provider selection: insights from a Turkish automotive company. Supply Chain Management: An International Journal.
  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274-289.
  • Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & Antuchevičienė, J. (2017). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66-78.
  • Leahy, S. E., Murphy, P. R., & Poist, R. F. (1995). Determinants of successful logistical relationships: a third-party provider perspective. Transportation Journal, 5-13.
  • 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.
  • Liu, P. D., & Wang, P. (2018). Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple-Attribute Decision Making. International Journal of Intelligent Systems, 33(2), 259-280. doi:10.1002/int.21927
  • Marasco, A. (2008). Third-party logistics: A literature review. International Journal of production economics, 113(1), 127-147.
  • Meade, L., & Sarkis, J. (2002). A conceptual model for selecting and evaluating third‐party reverse logistics providers. Supply Chain Management: An International Journal.
  • Min, H., & Joo, S. J. (2006). Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis. Supply chain management: An International journal.
  • Murphy, P. R., & Poist, R. F. (1998). Third-party logistics usage: an assessment of propositions based on previous research. Transportation Journal, 37(4), 26-35.
  • 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.
  • Pinar, A., & Boran, F. E. (2020). A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure. International Journal of Machine Learning and Cybernetics, 11(8), 1749-1780.
  • Selviaridis, K., & Spring, M. (2007). Third party logistics: a literature review and research agenda. The international journal of logistics management.
  • Wang, R., & Li, Y. (2018). A Novel Approach for Green Supplier Selection under a q-Rung Orthopair Fuzzy Environment. Symmetry, 10(12), 687. doi:https://doi.org/10.3390/sym10120687
  • Wei, G., Gao, H., & Wei, Y. (2018). Some q‐rung orthopair fuzzy Heronian mean operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(7), 1426-1458. doi:https://doi.org/10.1002/int.21985
  • Yager, R. R. (2017). Generalized Orthopair Fuzzy Sets. Ieee Transactions on Fuzzy Systems, 25(5), 1222-1230. doi:10.1109/Tfuzz.2016.2604005
  • 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.
  • Zadeh, L. A. (1965). Fuzzy sets. Information control, 8(3), 338-353. doi:https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zhou, G., Min, H., Xu, C., & Cao, Z. (2008). Evaluating the comparative efficiency of Chinese third‐party logistics providers using data envelopment analysis. International Journal of physical distribution & logistics management.
  • Torağay, O., & Arikan, M. (2015). Performance Evaluation of Faculty Departments by a Delphi Method Based on 2-Tuple fuzzy Linguistic Representation Model and TOPSIS. International Journal of Basic & Applied Sciences, 15, 1-10. URL: http://ijens.org/Vol_15_I_06/150205-7373-IJBAS-IJENS.pdf
Yıl 2021, Cilt: 5 Sayı: 1, 588 - 597, 30.06.2021

Öz

Kaynakça

  • Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69-78.
  • Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal.
  • 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.
  • Ecer, F. (2018). Third-party logistics (3PLs) provider selection via Fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615–634-615–634.
  • Efendigil, T., Önüt, S., & Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54(2), 269-287.
  • Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2016). A grey DEMATEL approach to develop third-party logistics provider selection criteria. Industrial Management & Data Systems.
  • Göl, H., & Çatay, B. (2007). Third‐party logistics provider selection: insights from a Turkish automotive company. Supply Chain Management: An International Journal.
  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274-289.
  • Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & Antuchevičienė, J. (2017). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66-78.
  • Leahy, S. E., Murphy, P. R., & Poist, R. F. (1995). Determinants of successful logistical relationships: a third-party provider perspective. Transportation Journal, 5-13.
  • 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.
  • Liu, P. D., & Wang, P. (2018). Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple-Attribute Decision Making. International Journal of Intelligent Systems, 33(2), 259-280. doi:10.1002/int.21927
  • Marasco, A. (2008). Third-party logistics: A literature review. International Journal of production economics, 113(1), 127-147.
  • Meade, L., & Sarkis, J. (2002). A conceptual model for selecting and evaluating third‐party reverse logistics providers. Supply Chain Management: An International Journal.
  • Min, H., & Joo, S. J. (2006). Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis. Supply chain management: An International journal.
  • Murphy, P. R., & Poist, R. F. (1998). Third-party logistics usage: an assessment of propositions based on previous research. Transportation Journal, 37(4), 26-35.
  • 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.
  • Pinar, A., & Boran, F. E. (2020). A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure. International Journal of Machine Learning and Cybernetics, 11(8), 1749-1780.
  • Selviaridis, K., & Spring, M. (2007). Third party logistics: a literature review and research agenda. The international journal of logistics management.
  • Wang, R., & Li, Y. (2018). A Novel Approach for Green Supplier Selection under a q-Rung Orthopair Fuzzy Environment. Symmetry, 10(12), 687. doi:https://doi.org/10.3390/sym10120687
  • Wei, G., Gao, H., & Wei, Y. (2018). Some q‐rung orthopair fuzzy Heronian mean operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(7), 1426-1458. doi:https://doi.org/10.1002/int.21985
  • Yager, R. R. (2017). Generalized Orthopair Fuzzy Sets. Ieee Transactions on Fuzzy Systems, 25(5), 1222-1230. doi:10.1109/Tfuzz.2016.2604005
  • 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.
  • Zadeh, L. A. (1965). Fuzzy sets. Information control, 8(3), 338-353. doi:https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zhou, G., Min, H., Xu, C., & Cao, Z. (2008). Evaluating the comparative efficiency of Chinese third‐party logistics providers using data envelopment analysis. International Journal of physical distribution & logistics management.
  • Torağay, O., & Arikan, M. (2015). Performance Evaluation of Faculty Departments by a Delphi Method Based on 2-Tuple fuzzy Linguistic Representation Model and TOPSIS. International Journal of Basic & Applied Sciences, 15, 1-10. URL: http://ijens.org/Vol_15_I_06/150205-7373-IJBAS-IJENS.pdf
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Adem Pınar 0000-0003-0471-7204

Yayımlanma Tarihi 30 Haziran 2021
Gönderilme Tarihi 22 Aralık 2020
Kabul Tarihi 31 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 1

Kaynak Göster

APA Pınar, A. (2021). q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. Journal of Turkish Operations Management, 5(1), 588-597.
AMA Pınar A. q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. JTOM. Haziran 2021;5(1):588-597.
Chicago Pınar, Adem. “Q-Rung Orthopair Fuzzy TOPSIS Application for 3rd Party Logistics Provider Selection”. Journal of Turkish Operations Management 5, sy. 1 (Haziran 2021): 588-97.
EndNote Pınar A (01 Haziran 2021) q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. Journal of Turkish Operations Management 5 1 588–597.
IEEE A. Pınar, “q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection”, JTOM, c. 5, sy. 1, ss. 588–597, 2021.
ISNAD Pınar, Adem. “Q-Rung Orthopair Fuzzy TOPSIS Application for 3rd Party Logistics Provider Selection”. Journal of Turkish Operations Management 5/1 (Haziran 2021), 588-597.
JAMA Pınar A. q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. JTOM. 2021;5:588–597.
MLA Pınar, Adem. “Q-Rung Orthopair Fuzzy TOPSIS Application for 3rd Party Logistics Provider Selection”. Journal of Turkish Operations Management, c. 5, sy. 1, 2021, ss. 588-97.
Vancouver Pınar A. q-Rung orthopair fuzzy TOPSIS application for 3rd party logistics provider selection. JTOM. 2021;5(1):588-97.

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