IVPF-AHP integrated VIKOR methodology in supplier selection of three-dimensional (3D) printers

Abstract

Complex geometries, fine details, and various designs that are difficult to create using traditional methods can easily be turned into a tangible object with Three-Dimensional (3D) printers. 3D printers have advantages such as providing design flexibility, obtaining prototypes in the shortest possible time, allowing for personalization, and reducing waste through the use of advanced technology. These advantages emphasize the significance of 3D printers in a sustainable production model. The widespread usage of 3D printers leads to increased efficiency and cost reduction in production. When the literature is examined, it is observed that there are limited studies on the evaluation of supplier performances for company using 3D printers. The aim of this study is to address 3D printers, which are highly significant for sustainable production, and to reveal the criteria that companies utilizing these printers need to consider for determining their suppliers. As a result of the literature review and expert interviews, a model has been developed that gathers the criteria to be considered for supplier selection, which is an important cost factor for companies involved in designing and producing 3D printers under five main and 18 sub-criteria. The importance weights of the criteria have been determined using the Interval Valued Pythagorean Fuzzy Analytic Hierarchy Process (IVPF-AHP) method, and the most suitable supplier among alternative suppliers has been selected using the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, the supplier scores have been statistically analyzed to show the validation of the results of the proposed method. According to the results, it has been concluded that for company using 3D printers, quality and technical service criteria are more important in the supplier selection. Additionally, cost of the material/equipment, product price and easy maintenance criteria also play a critical role in the supplier selection of 3D printer.

Keywords

• 3D printer
• Supplier selection
• IVPF-AHP
• VIKOR

References

1. Fauth, J., Elkaseer, A., & Scholz, S. G. (2019). Total cost of ownership for different state of the art FDM machines (3D printers). In International Conference on Sustainable Design and Manufacturing, 351-361. https://doi.org/10.1007/978-981-13-9271-9_29
2. Özgüner, M., & Özgüner, Z. (2022). Evaluation of the importance of additive manufacturing technology in terms of sustainable production with the DEMATEL method. International Journal of Advanced and Applied Sciences, 9(10), 116-125. https://doi.org/10.21833/ijaas.2022.10.015
3. Negi, S., Dhiman, S., & Sharma, R. K. (2013). Basics, applications and future of additive manufacturing technologies: A review. Journal of Manufacturing Technology Research, 5(1/2), 75-96.
4. Çetinkaya, C., Kabak, M., & Özceylan, E. (2017). 3D printer selection by using fuzzy analytic hierarchy process and PROMETHEE. Bilişim Teknolojileri Dergisi, 10(4), 371-380. https://doi.org/10.17671/gazibtd.347610
5. Villi, O., Villi, Ö., & Yakar, M. (2023). 3 Boyutlu Yazıcıların İnsansız Hava Aracı Uygulamalarında Kullanımı. Türkiye İnsansız Hava Araçları Dergisi, 5(2), 72-88. https://doi.org/10.51534/tiha.1315188
6. Jermsittiparsert, K., Zahar, M., Sumarni, S., Voronkova, O. Y., Bakhvalov, S. Y., & Akhmadeev, R. (2021). Selection of sustainable suppliers in the oil and gas industry using fuzzy multi-criteria decision-making methods. International Journal of Industrial Engineering and Management, 12(4), 253-261. https://doi.org/10.24867/IJIEM-2021-4-292
7. Çebi, S., Onar, S. Ç., Öztayşi, B., & Kahraman, C. (2022). Integration of Analytic Hierarchy Process with Other MCDM Methods: A Literature Review. In International Symposium on the Analytic Hierarchy Process, ISAHP, 1-6.
8. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.

9. Churchman, C. W., & Ackoff, R. L. (1954). An approximate measure of value. Journal of the Operations Research Society of America, 2(2), 172-187. https://doi.org/10.1287/opre.2.2.172
10. Fishburn, P. C. (1968). Utility theory. Management Science, 14(5), 335–378. https://doi.org/10.1287/mnsc.14.5.335
11. Benayoun, R., Roy, B., & Sussman, N. (1966). Manual de reference du programme electre. Note de synthese et Formation, 25(79).
12. Fishburn, P. C. (1967). Methods of estimating additive utilities. Management science, 13(7), 435-453. https://doi.org/10.1287/mnsc.13.7.435
13. Miller, D. W. (1963). Executive decisions and operations research. Englewood Cliffs, Prentice-Hall, Inc, NJ, U.S.A.
14. Fontela, E., & Gabus, A. (1976). The Dematel observer. Switzerland Geneva: Battelle Geneva Research Center, USA
15. Edwards, W. (1977). How to use multiattribute utility measurement for social decisionmaking. IEEE transactions on systems, man, and cybernetics, 7(5), 326-340. https://doi.org/10.1109/TSMC.1977.4309720
16. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. https://doi.org/10.1016/0022-2496(77)90033-5
17. Hwang, C. L., & Yoon, K. (2012). Multiple attribute decision making: methods and applications a state-of-the-art survey, 186. Springer Science & Business Media.
18. Brans, J. P., & Vincke, P. (1985). Note—A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management science, 31(6), 647-656. https://doi.org/10.1287/mnsc.31.6.647
19. Mareschal, B., Brans, J. P., & Vincke, P. (1984). Promethee: A new family of outranking methods in multicriteria analysis. ULB--Universite Libre de Bruxelles.
20. Bana e Costa, C. A., & Vansnick, J. C. (1999). The MACBETH approach: Basic ideas, software, and an application. In Advances in decision analysis, 131-157. https://doi.org/10.1007/978-94-017-0647-6_9
21. Kaklauskas, A., Zavadskas, E. K., Raslanas, S., Ginevicius, R., Komka, A., & Malinauskas, P. (2006). Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy and buildings, 38(5), 454-462. https://doi.org/10.1016/j.enbuild.2005.08.005
22. Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process, 4922, 2.
23. Deng, J. L. (1982). Control problems of grey system. System & Control Letters, 1, 288-294.
24. Opricovic, S., & Tzeng, G. H. (2002). Multicriteria planning of post‐earthquake sustainable reconstruction. Computer‐Aided Civil and Infrastructure Engineering, 17(3), 211-220. https://doi.org/10.1111/1467-8667.00269
25. Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5-24. https://doi.org/10.3846/tede.2010.01
26. Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and cybernetics, 35(2), 445-469.
27. Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Ukio Technologinis Ir Ekonominis Vystymas, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10
28. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. https://doi.org/10.5755/j01.eee.122.6.1810
29. Keshavarz Ghorabaee, M., 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.
30. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
31. Menekse, A., Ertemel, A. V., Camgoz Akdag, H., & Gorener, A. (2023). Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS. Plos one, 18(3), e0282676. https://doi.org/10.1371/journal.pone.0282676
32. Büyüközkan, G., & Göçer, F. (2020). Assessment of additive manufacturing technology by pythagorean fuzzy CODAS. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019, 959-968. https://doi.org/10.1007/978-3-030-23756-1_114
33. Sahoo, S. K., & Goswami, S. S. (2024). Green Supplier Selection using MCDM: A Comprehensive Review of Recent Studies. Spectrum of Engineering and Management Sciences, 2(1), 1-16. https://doi.org/10.31181/sems1120241a
34. Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2021). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development & World Ecology, 28(2), 125-142. https://doi.org/10.1080/13504509.2020.1793424
35. Więckowski, J., Kizielewicz, B., Shekhovtsov, A., & Sałabun, W. (2023). How do the criteria affect sustainable supplier evaluation?-A case study using multi-criteria decision analysis methods in a fuzzy environment. Journal of Engineering Management and Systems Engineering, 2(1), 37-52. https://doi.org/10.56578/jemse020102.
36. El-Morsy, S. (2023). Stock portfolio optimization using pythagorean fuzzy numbers. Journal of Operational and Strategic Analytics, 1(1), 8-13. https://doi.org/10.56578/josa010102
37. Yazdani, M., Torkayesh, A. E., Stević, Ž., Chatterjee, P., Ahari, S. A., & Hernandez, V. D. (2021). An interval valued neutrosophic decision-making structure for sustainable supplier selection. Expert Systems with Applications, 183, 115354. https://doi.org/10.1016/j.eswa.2021.115354
38. Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
39. Su, C. M., Horng, D. J., Tseng, M. L., Chiu, A. S., Wu, K. J., & Chen, H. P. (2016). Improving sustainable supply chain management using a novel hierarchical grey-DEMATEL approach. Journal of Cleaner Production, 134, 469-481. https://doi.org/10.1016/j.jclepro.2015.05.080
40. Nagarajan, D., Gobinath, V. M., & Broumi, S. (2023). Multicriteria Decision Making on 3D printers for economic manufacturing using Neutrosophic environment. Neutrosophic Sets and Systems, 57, 33-56.
41. Ayyildiz, E., & Taskin Gumus, A. (2021). Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0. Complex & Intelligent Systems, 7(1), 559-576. https://doi.org/10.1007/s40747-020-00221-9
42. Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514-529. https://doi.org/10.1016/j.ejor.2006.01.020
43. Vimal, K. E. K., Vinodh, S., Brajesh, P., & Muralidharan, R. (2016). Rapid prototyping process selection using multi criteria decision making considering environmental criteria and its decision support system. Rapid Prototyping Journal, 22(2), 225-250. https://doi.org/10.1108/RPJ-03-2014-0040
44. Anand, M. B., & Vinodh, S. (2018). Application of fuzzy AHP–TOPSIS for ranking additive manufacturing processes for microfabrication. Rapid Prototyping Journal, 24(2), 424-435. https://doi.org/10.1108/RPJ-10-2016-0160
45. Khamhong, P., Yingviwatanapong, C., & Ransikarbum, K. (2019). Fuzzy analytic hierarchy process (AHP)-based criteria analysis for 3D printer selection in additive manufacturing. In 2019 Research, Invention, and Innovation Congress (RI2C), 1-5. https://doi.org/10.1109/RI2C48728.2019.8999950
46. Prabhu, S. R., & Ilangkumaran, M. (2019). Decision making methodology for the selection of 3D printer under fuzzy environment. International Journal of Materials and Product Technology, 59(3), 239-252. https://doi.org/10.1504/IJMPT.2019.102935
47. Vinodh, S., Nagaraj, S., & Girubha, J. (2014). Application of Fuzzy VIKOR for selection of rapid prototyping technologies in an agile environment. Rapid Prototyping Journal, 20(6), 523-532. https://doi.org/10.1108/RPJ-07-2012-0060
48. Mançanares, C. G., de S. Zancul, E., Cavalcante da Silva, J., & Cauchick Miguel, P. A. (2015). Additive manufacturing process selection based on parts’ selection criteria. The International Journal of Advanced Manufacturing Technology, 80, 1007-1014. https://doi.org/10.1007/s00170-015-7092-4
49. Uz Zaman, U. K., Rivette, M., Siadat, A., & Mousavi, S. M. (2018). Integrated product-process design: Material and manufacturing process selection for additive manufacturing using multi-criteria decision making. Robotics and Computer-Integrated Manufacturing, 51, 169-180. https://doi.org/10.1016/j.rcim.2017.12.005
50. Prabhu, S. R., & Ilangkumaran, M. (2019). Selection of 3D printer based on FAHP integrated with GRA-TOPSIS. International Journal of Materials and Product Technology, 58(2-3), 155-177. https://doi.org/10.1504/IJMPT.2019.097667
51. Shende, V., & Kulkarni, P. (2014). Decision support system for rapid prototyping process selection. International Journal of Scientific and Research Publications, 4(1), 2250-3153.
52. Nguyen, N. D., Ashraf, I., & Kim, W. (2021). Compact model for 3d printer energy estimation and practical energy-saving strategy. Electronics, 10(4), 483. https://doi.org/10.3390/electronics10040483
53. Junwen, C., Gang, Z., & Hua, Z. (2019). Energy consumption prediction of fused deposition 3D printer based on improved regularized BP neural network. In Iop Conference Series: Earth and Environmental Science, 295(3), 032001. https://doi.org/10.1088/1755-1315/295/3/032001
54. Aydoğdu, A., & Gül, S. (2022). New entropy propositions for interval‐valued spherical fuzzy sets and their usage in an extension of ARAS (ARAS‐IVSFS). Expert Systems, 39(4), e12898. https://doi.org/10.1111/exsy.12898
55. Zagidullin, R., Mitroshkina, T., & Dmitriev, A. (2021). Quality function deployment and design risk analysis for the selection and improvement of FDM 3D printer. In IOP conference series: earth and environmental science, 666(6), 062123. https://doi.org/10.1088/1755-1315/666/6/062123
56. Ko, C. H. (2018). Quality Requirements and Satisfaction of Consumer 3D Printers. In 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI), 712-717. https://doi.org/10.1109/IIAI-AAI.2018.00148
57. Ashima, R., Haleem, A., Bahl, S., Javaid, M., Mahla, S. K., & Singh, S. (2021). Automation and manufacturing of smart materials in Additive Manufacturing technologies using Internet of Things towards the adoption of Industry 4.0. Materials Today: Proceedings, 45, 5081-5088. https://doi.org/10.1016/j.matpr.2021.01.583
58. Ranjan, R., Kumar, D., Kundu, M., & Moi, S. C. (2022). A critical review on Classification of materials used in 3D printing process. Materials today: proceedings, 61, 43-49. https://doi.org/10.1016/j.matpr.2022.03.308
59. Chatterjee, S., & Chakraborty, S. (2023). A Multi-criteria decision making approach for 3D printer nozzle material selection. Reports in Mechanical Engineering, 4(1), 62-79. https://doi.org/10.31181/rme040121042023c
60. Mirón, V., Ferràndiz, S., Juàrez, D., & Mengual, A. (2017). Manufacturing and characterization of 3D printer filament using tailoring materials. Procedia Manufacturing, 13, 888-894. https://doi.org/10.1016/j.promfg.2017.09.151
61. Vongvit, R. (2015). Using the Fuzzy-QFD for Product Development: A case study for 3D Printer. Applied Mechanics and Materials, 789, 1196-1200. https://doi.org/10.4028/www.scientific.net/AMM.789-790.1196
62. Habib, T., Omair, M., Habib, M. S., Zahir, M. Z., Khattak, S. B., Yook, S. J., ... & Akhtar, R. (2023). Modular Product Architecture for Sustainable Flexible Manufacturing in Industry 4.0: The Case of 3D Printer and Electric Toothbrush. Sustainability, 15(2), 910. https://doi.org/10.3390/su15020910
63. Yuran, A. F., & Yavuz, İ. (2021). Industry 4.0 and Comparison Of 3D Printers. Mühendis ve Makina, 62, 580-606.
64. Naghshineh, B., & Carvalho, H. (2022). The implications of additive manufacturing technology adoption for supply chain resilience: A systematic search and review. International Journal of Production Economics, 247, 108387. https://doi.org/10.1016/j.ijpe.2021.108387
65. Feldmann, C., & Pumpe, A. (2017). A holistic decision framework for 3D printing investments in global supply chains. Transportation research procedia, 25, 677-694. https://doi.org/10.1016/j.trpro.2017.05.451
66. Eddous, S., Lamé, G., Decante, B., Yannou, B., Agathon, A., Aubrège, L., ... & Dacosta-Noble, É. (2023). Current and potential applications of 3D printing in a general hospital. Proceedings of the Design Society, 3, 1117-1126. https://doi.org/10.1017/pds.2023.112
67. Koskin, V., & Nguyen, T. T. V. (2021). The impact of Industry 4.0 on supply chain management. LAB University of Applied Sciences Bachelor of International Business.
68. Daya, T. (2017). Facilitating sustainable material decisions: a case study of 3D printing materials. [Doctoral dissertation, University of California, Berkeley].
69. Chen, T. C. T., & Lin, Y. C. (2021). Diverse three-dimensional printing capacity planning for manufacturers. Robotics and Computer-Integrated Manufacturing, 67, 102052. https://doi.org/10.1016/j.rcim.2020.102052
70. de Brito, F. M., da Cruz Júnior, G., Frazzon, E. M., Basto, J. P., & Alcalá, S. G. (2019). An optimization model for the design of additive manufacturing supply chains. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 1, 881-885. https://doi.org/10.1109/INDIN41052.2019.8972028
71. Wits, W. W., García, J. R. R., & Becker, J. M. J. (2016). How additive manufacturing enables more sustainable end-user maintenance, repair and overhaul (MRO) strategies. Procedia Cirp, 40, 693-698. https://doi.org/10.1016/j.procir.2016.01.156
72. Aydin, S., & Kahraman, C. (2010). Multiattribute supplier selection using fuzzy analytic hierarchy process. International Journal of Computational Intelligence Systems, 3(5), 553-565. https://doi.org/10.1080/18756891.2010.9727722
73. Joo, Y., Shin, I., Ham, G., Abuzar, S. M., Hyun, S. M., & Hwang, S. J. (2020). The advent of a novel manufacturing technology in pharmaceutics: Superiority of fused deposition modeling 3D printer. Journal of Pharmaceutical Investigation, 50, 131-145. https://doi.org/10.1007/s40005-019-00451-1
74. Zadeh, L. A. (1965). Information and control. Fuzzy Sets, 8(3), 338-353.
75. Atanassov, K. T., & Atanassov, K. T. (1999). Intuitionistic fuzzy sets. https://doi.org/10.1007/978-3-7908-1870-3_1
76. Yager, R. R. (2013). Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 57-61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
77. Kahraman, C., & Cebi, S. (Eds.). (2023). Analytic Hierarchy Process with Fuzzy Sets Extensions: Applications and Discussions, 428. Springer Nature.
78. Van Laarhoven, P. J., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy sets and Systems, 11(1-3), 229-241. https://doi.org/10.1016/S0165-0114(83)80082-7
79. Karaşan, A., Gündoğdu, F. K., & Kahraman, C. (2020). Pythagorean fuzzy AHP method for the selection of the most appropriate clean energy technology. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019, 879-887. https://doi.org/10.1007/978-3-030-23756-1_105
80. Otay, I., & Jaller, M. (2020). Multi-criteria and multi-expert wind power farm location selection using a pythagorean fuzzy analytic hierarchy process. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019, 905-914. https://doi.org/10.1007/978-3-030-23756-1_108
81. Ilbahar, E., Cebi, S., & Kahraman, C. (2020). Assessment of renewable energy alternatives with pythagorean fuzzy WASPAS method: a case study of Turkey. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019, 888-895. https://doi.org/10.1007/978-3-030-23756-1_106
82. Mete, S. (2019). Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment. Human and Ecological Risk Assessment: An International Journal, 25(7), 1645-1660. https://doi.org/10.1080/10807039.2018.1546115
83. Oz, N. E., Mete, S., Serin, F., & Gul, M. (2018). Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards. Human and Ecological Risk Assessment: An International Journal, 25(6), 1615-1632. https://doi.org/10.1080/10807039.2018.1495057
84. Bolturk, E., & Kahraman, C. (2018). Natural gas technology selection using Pythagorean fuzzy CODAS. In Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018), 1034-1041. https://doi.org/10.1142/9789813273238_0131
85. Mete, S., Serin, F., Oz, N. E., & Gul, M. (2019). A decision-support system based on Pythagorean fuzzy VIKOR for occupational risk assessment of a natural gas pipeline construction. Journal of Natural Gas Science and Engineering, 71, 102979. https://doi.org/10.1016/j.jngse.2019.102979
86. Wood, D. A. (2016). Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. Journal of Natural Gas Science and Engineering, 28, 594-612. https://doi.org/10.1016/j.jngse.2015.12.021
87. Yildiz, A., Ayyildiz, E., Taskin Gumus, A., & Ozkan, C. (2020). A modified balanced scorecard based hybrid pythagorean fuzzy AHP-topsis methodology for ATM site selection problem. International Journal of Information Technology & Decision Making, 19(02), 365-384. https://doi.org/10.1142/S0219622020500017
88. Coşkun, R., Kiriş, Z. N., & Tepe, S. N. (2019). A new fuzzy based marketing performance measurement model with a real case study. Econder International Academic Journal, 3(1), 41-73. https://doi.org/10.35342/econder.549834
89. Erdoğan, N. K., Onay, A., & Karamaşa, Ç. (2019). Measuring the performance of retailer firms listed in BIST under the balanced scorecard perspective by using interval valued Pythagorean Fuzzy AHP based Pythagorean Fuzzy TODIM Methodology. Alphanumeric Journal, 7(2), 333-350. https://doi.org/10.17093/alphanumeric.451247
90. Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety science, 103, 124-136. https://doi.org/10.1016/j.ssci.2017.10.025
91. Yager, R. R. (2016). Properties and applications of Pythagorean fuzzy sets. Imprecision and Uncertainty in Information Representation and Processing: New Tools Based on Intuitionistic Fuzzy Sets and Generalized Nets, 119-136. https://doi.org/10.1007/978-3-319-26302-1_9
92. Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International Journal of Intelligent Systems, 29(12), 1061-1078. https://doi.org/10.1002/int.21676
93. Peng, X., & Yang, Y. (2016). Fundamental properties of interval‐valued Pythagorean fuzzy aggregation operators. International Journal of Intelligent Systems, 31(5), 444-487. https://doi.org/10.1002/int.21790
94. Haktanır, E., & Kahraman, C. (2020). Malcolm baldrige national quality award assessment using interval valued pythagorean fuzzy sets. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019, 1097-1103. https://doi.org/10.1007/978-3-030-23756-1_129
95. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455. https://doi.org/10.1016/S0377-2217(03)00020-1
96. Uygun, Ö., Kahvecı, T. C., Taşkın, H., & Priştine, B. (2013). A model for measuring institutionalization level of SMEs. TOJSAT, 3(4), 1-17.
97. Alili, A., & Krstev, D. (2019). Using spss for research and data analysis. Knowledge–International Journal, 32(3), 301-390
98. Kimani, C. J., & Scott, J. (2023). Advanced SPSS Professional Leve. Finstock Evarsity Publishers.
99. Taylor, R. (1990). Interpretation of the correlation coefficient: a basic review. Journal of diagnostic medical sonography, 6(1), 35-39. https://doi.org/10.1177/875647939000600106