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An Enhanced Intuitionistic Fuzzy TOPSIS approach Based on Yager’s Aggregation Operators and the Application in Selecting Green Suppliers Cover

An Enhanced Intuitionistic Fuzzy TOPSIS approach Based on Yager’s Aggregation Operators and the Application in Selecting Green Suppliers

Open Access
|Jun 2026

References

  1. Aggarwal A., Gupta A., Verma R., Kumari R., Ranking of efficient fuzzy portfolios by hybrid MSBM-TOPSIS technique, Foundations of Computing and Decision Sciences, 50, 1, 2025, 1–24.
  2. Ahmadi H.B., Hashemi S., Petrudi S.H., and Wang X., Integrating sustainability into supplier selection with analytical hierarchy process and improved grey relational analysis: a case of telecom industry, The International Journal of Advanced Manufacturing Technology, 90, 2017, 2413-2427.
  3. Ali H., Zhang J., Liu S. Shoaib M., An integrated decision-making approach for global supplier selection and order allocation to create an environmentally friendly supply chain, Kybernetes, 52, 8, 2023, 2649–2671.
  4. Atanassov K. T., Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, 1, 1986, 87–96.
  5. Burillo P., Bustince H., Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets, Fuzzy Sets and Systems, 78, 3, 1996, 305-316.
  6. Büyüközkan G., and Çifçi G., A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers, Expert Systems with Applications, 39, 3, 2012, 3000-3011.
  7. Çakır S., Selecting the best supplier at a steel-producing company under fuzzy environment, The International Journal of Advanced Manufacturing Technology, 88, 2017, 1345-1361.
  8. Chen C.T., Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, 114, 1, 2000, 1–9.
  9. Chen S. M., Chen S.W., Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships, IEEE Transactions on Cybernetics, 45, 3, 2014, 391-403.
  10. De S. K., Biswas R., Roy A. R., An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy sets and Systems, 117, 2, 2001, 209-213.
  11. Ecer F., Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer, Operational Research, 22, 1, 2022, 199-233.
  12. Garg H., Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making, Computers & Industrial Engineering, 101, 2016, 53–69.
  13. Garg H., Novel intuitionistic fuzzy decision-making method based on an improved operation laws and its application, Engineering Applications of Artificial Intelligence, 60, 2017,164–174.
  14. Huang J. Y., Intuitionistic fuzzy Hamacher aggregation operators and their application to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems, 27, 1, 2014, 505–513.
  15. Humphreys P., Huang G., Cadden T., McIvor R., Integrating design metrics within the early supplier selection process, Journal of Purchasing and Supply Management, 13, 1, 2007, 42-52.
  16. Hwang C. L., Yoon K., Methods for multiple attribute decision making, in Multiple Attribute Decision Making: Methods and Applications—A State-of-the-Art Survey, Lecture Notes in Economics and Mathematical Systems, vol. 186, Berlin, Germany: Springer, pp. 58–191, 1981.
  17. Jin J., Garg H., Intuitionistic fuzzy three-way ranking-based TOPSIS approach with a novel entropy measure and its application to medical treatment selection, Advances in Engineering Software, 180, 2023, Article no.103459.
  18. Joshi D., Kumar S., Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making, Egyptian Informatics Journal, 15, 2, 2014, 97-104.
  19. Li D. F., Multi-attribute decision making method based on generalized OWA operators with intuitionistic fuzzy sets, Expert Systems with Applications, 37, 12, 2010, 8673-8678.
  20. Medić N., Anišić Z., Lalić B., Marjanović U., Brezocnik M., Hybrid fuzzy multi-attribute decision making model for evaluation of advanced digital technologies in manufacturing: Industry 4.0 perspective, Advances in Production Engineering and Management,14, 4, 2019, 483-493.
  21. Mousakhani S., Nazari-Shirkouhi S., Bozorgi-Amiri A., A novel interval type-2 fuzzy evaluation model-based group decision analysis for green supplier selection problems: A case study of battery industry, Journal of Cleaner Production, 168, 2017,205-218.
  22. Onat N. C., Gumus S., Kucukvar M., Tatari O., Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies, Sustainable Production and Consumption, 6, 2016, 12–25.
  23. Rahman K., Abdullah S., Jamil M., Khan M. Y., Some generalized intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute group decision making, International Journal of Fuzzy Systems, 20, 2018, 1567–1575.
  24. Rouyendegh B. D., Yildizbasi A., Üstünyer P., Intuitionistic fuzzy TOPSIS method for green supplier selection problem, Soft Computing, 24, 2020, 2215–2228.
  25. Roy M. K., Shivakoti I., Phipon R., Sharma A., A holistic approach to polymeric material selection for laser beam machining using methods of DEA and TOPSIS, Foundations of Computing and Decision Sciences, 45, 4, 2020, 339–357.
  26. Shen F., Ma X., Li Z., Xu Z., Cai D., An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation, Information Sciences, 428, 2018, 105–119.
  27. Seikh M. R., Mandal U., Intuitionistic fuzzy Dombi aggregation operators and their application to multiple attribute decision-making, Granular Computing, 6, 2021, 473–488.
  28. Tan A., Shi S., Wu W.Z., Li J., Pedrycz W., Granularity and entropy of intuitionistic fuzzy information and their applications, IEEE Transactions on Cybernetics, 52, 1, 2022, 192–204.
  29. Verma R., Chandra S., Interval-valued intuitionistic fuzzy-analytic hierarchy process for evaluating the impact of security attributes in fog-based internet of things paradigm, Computer Communications, 175, 2021, 35-46.
  30. Wang W., Liu X., Intuitionistic fuzzy geometric aggregation operators based on Einstein operations, International Journal of Intelligent Systems, 26, 11, 2011, 1049-1075.
  31. Wang W., Liu X., Intuitionistic fuzzy information aggregation using Einstein operations, IEEE Transactions on Fuzzy Systems, 20, 5, 2012, 923–938.
  32. Wei G., Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making, Applied Soft Computing,10, 2, 2010, 423-431.
  33. Xu Z. Intuitionistic fuzzy aggregation operators, IEEE Transactions on Fuzzy Systems, 15, 6, 2007, 1179-1187.
  34. Xu Z. Yager R. R., Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems, 35, 4, 2006, 417-433.
  35. Yager R. R., On a general class of fuzzy connectives, Fuzzy Sets and Systems, 4, 1980, 235–242.
  36. Yang M. S., Hussain Z., Mehboob A., Belief and plausibility measures on intuitionistic fuzzy sets with construction of belief–plausibility TOPSIS, Complexity, 2020, 2020, Article ID 8895546.
  37. Yazdani M., Chatterjee P., Zavadskas E.K., Zolfani S.H., Integrated QFD-MCDM framework for green supplier selection, Journal of Cleaner Production, 142, 2017, 3728-3740.
  38. Yue Z., An extended TOPSIS for determining weights of decision makers with interval numbers, Knowledge-Based Systems, 24, 1, 2011, 146–153.
  39. Zhao X., Wei G., Some intuitionistic fuzzy Einstein hybrid aggregation operators and their application to multiple attribute decision making, Knowledge-Based Systems, 37, 2013, 472-479.
  40. Zhu Y. J., Li D. F., A new definition and formula of entropy for intuitionistic fuzzy sets, Journal of Intelligent & Fuzzy Systems, 30, 6, 2016, 3057–3066.
DOI: https://doi.org/10.2478/fcds-2026-0006 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 173 - 212
Submitted on: Mar 19, 2025
Accepted on: Dec 23, 2025
Published on: Jun 26, 2026
In partnership with: Paradigm Publishing Services

© 2026 Lining Lai, Zia Ullah, Muhammad Tariq Rahim, Qaisar Khan, Fawad Hussain, Peide Liu, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.