Intuitionistic Fuzzy TOPSIS-Based Hardware Selection for Industrial Robots Under Conflicting Quantitative Criteria

This study successfully applied an Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) framework to resolve the industrial robot hardware selection problem under conflicting criteria. By incorporating non-membership () and hesitation () degrees alongside standard membership (), the proposed approach effectively models uncertainty and expert judgment hesitation inherent in major equipment procurement decisions. The numerical analysis proves that IF-TOPSIS provides a transparent, objective, and mathematically reliable decision-support system for manufacturing managers. Future research can extend this work by incorporating dynamic weighting methods or integrating Pythagorean Fuzzy Sets (PFS) for higher variance domains.

1.                Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. 2.                 Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368. 3.                P. N. Kamble and Naziya Parveen (2022). Extended TOPSIS and VIKOR methods-based om a new distance measure of intuitionistic fuzzy number, Artificial intelligence and sustainable computing, springer nature, Singapore Pte Ltd, 495-510. 4.                Naziya Parveen and P. N Kamble (2021). An Extension of TOPSIS for group decision making in Intuitionistic Fuzzy environment, Mathematical Foundation of Computing, American Institute of Mathematical Science, 4(1), 61-71. 5.                Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. 6.                 Chu, T. C., & Lin, Y. C. (2003). A fuzzy TOPSIS method for robot selection. The International Journal of Advanced Manufacturing Technology, 21(4), 284–290. 7.                 Devi, K. (2011). Extension of VIKOR method in intuitionistic fuzzy environment for robot selection. Expert Systems with Applications, 38(11), 14163–14168. 8.                 Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag. 9.                Narayana Moorthy, S., Geetha, S., Rakkiyappan, R., & Joo, Y. H. (2019). Interval-valued intuitionistic hesitant fuzzy entropy-based VIKOR method for industrial robots’ selection. Expert Systems with Applications, 121, 28–37. 10.             Parameswaran, R., Kumar, S. P., & Saravanakumar, K. (2015). An integrated fuzzy MCDM based approach for robot selection considering objective and subjective criteria. Applied Soft Computing, 26, 31–41.