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

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.

Anahtar Kelimeler

• Multiple-criteria decision making methods
• third parthy logistics provider selection
• q-rung orthopair fuzzy sets
• TOPSIS

Kaynakça

1. Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69-78.
2. Bottani, E., & Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management: An International Journal.
3. 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.
4. 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.
5. 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.
6. Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2016). A grey DEMATEL approach to develop third-party logistics provider selection criteria. Industrial Management & Data Systems.
7. Göl, H., & Çatay, B. (2007). Third‐party logistics provider selection: insights from a Turkish automotive company. Supply Chain Management: An International Journal.
8. Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274-289.

9. 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.
10. Leahy, S. E., Murphy, P. R., & Poist, R. F. (1995). Determinants of successful logistical relationships: a third-party provider perspective. Transportation Journal, 5-13.
11. 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.
12. 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.
13. 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
14. Marasco, A. (2008). Third-party logistics: A literature review. International Journal of production economics, 113(1), 127-147.
15. Meade, L., & Sarkis, J. (2002). A conceptual model for selecting and evaluating third‐party reverse logistics providers. Supply Chain Management: An International Journal.
16. 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.
17. 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.
18. 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.
19. 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.
20. Selviaridis, K., & Spring, M. (2007). Third party logistics: a literature review and research agenda. The international journal of logistics management.
21. 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
22. 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
23. Yager, R. R. (2017). Generalized Orthopair Fuzzy Sets. Ieee Transactions on Fuzzy Systems, 25(5), 1222-1230. doi:10.1109/Tfuzz.2016.2604005
24. 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.
25. Zadeh, L. A. (1965). Fuzzy sets. Information control, 8(3), 338-353. doi:https://doi.org/10.1016/S0019-9958(65)90241-X
26. 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.
27. 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