TRIANGULAR INTUITIONISTIC FUZZY (TIF) TOPSIS AND STATISTICAL APPROACH FOR CONVENIENCE STORE PREFERENCE RANKING
DOI:
https://doi.org/10.46754/jmsi.2026.06.006Keywords:
Multi-Criteria Decision-Making (MCDM), Triangular Intuitionistic Fuzzy TOPSIS, convenience store selectionAbstract
Convenience store selection often involves subjective judgement and uncertainty, making fuzzy decision-making approaches highly suitable. This study employs the Triangular Intuitionistic Fuzzy TOPSIS (TIF-TOPSIS) method, which integrates membership and non-membership functions to better capture uncertainty in consumer preferences. Five convenience stores anonymised as Store A, Store B, Store C, Store D, and Store E, were assessed based on four benefit criteria (cleanliness, store image, product assortment and service quality) and one cost criterion (price). Data was collected from undergraduate students enrolled in a mathematics degree programme at a public higher-learning institution in the Klang Valley. The TIF-TOPSIS procedure was implemented in two phases: (i) Conversion of linguistic evaluations into triangular intuitionistic fuzzy numbers, followed by aggregation, normalisation, and weighting; and (ii) computation of the Intuitionistic Fuzzy Positive Ideal Solution (IFPIS), Intuitionistic Fuzzy Negative Ideal Solution (IFNIS), and the closeness coefficient for ranking alternatives. Results indicate that Store C is the most preferred option, followed by Store B, Store D, Store A, and Store E. Cleanliness and service quality emerged as the most important criteria, while price was identified as the key cost factor. The fuzzy findings were supported by statistical analysis, which produced results that were both consistent with the ranking patterns and of acceptable reliability. The integration of TIF-TOPSIS with statistical validation enhances decision reliability and provides a comprehensive assessment framework for convenience store selection.
References
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
Hwang, C. L., & Yoon, K. (2012). Multiple attribute decision making: Methods and applications — a state-of-the-art survey. Springer Science & Business Media.
Jaafri, N. A. H., & Mansor, A. A. (2024). Understanding customer satisfaction: A study of convenience stores. Information Management and Business Review, 16(3), 144–153. https://doi.org/10.22610/imbr.v16i3s(i)a.4206
Kamari, L. M., Isvand, H., & Alhuyi Nazari, M. (2020). Applications of multi-criteria decision-making (MCDM) methods in renewable energy development: A review. Renewable Energy Research and Applications, 1(1), 47–54. https://doi.org/10.22044/RERA.2020.8541.1006
Kumar, A. P., & Metta, S. (2024). Customer buying preferences towards retail stores. International Journal of Research Publication and Reviews, 5(2), 21–26. https://doi.org/10.55248/gengpi.5.0224.0421
Mahapatra, G. S., & Roy, T. K. (2009). Reliability evaluation using triangular intuitionistic fuzzy numbers arithmetic operations. World Academy of Science, Engineering and Technology, 50, 574-581. http://scholar.waset.org/1307-6892/10400
Mishra, A. R., Rani, P., Pamucar, D., Alshamrani, A. M., & Alrasheedi, A. F. (2025). Intuitionistic fuzzy MACONT method for logistics 4.0 based circular economy interested regions assessment in the agri-food sector. Facta Universitatis, Series: Mechanical Engineering, 23(3), 407–432. https://doi.org/10.22190/FUME241001052M
Mohan, S., Kannusamy, A. P., & Samiappan, V. (2020). A new approach for ranking of intuitionistic fuzzy numbers. Journal of Fuzzy Extension and Applications, 1(1), 15–26. http://dx.doi.org/10.22105/jfea.2020.247301.1003
Mukherjee, S., De, A., & Roy, S. (2024). Supply chain risk prioritization: A multi-criteria based intuitionistic fuzzy TOPSIS approach. International Journal of Quality & Reliability Management, 41(6), 1693–1725. https://doi.org/10.1108/IJQRM-07-2023-0214
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Ozsahin, D. U., Gökcekus, H., Uzun, B., & LaMoreaux, J. W. (Eds.). (2021). Application of multi-criteria decision analysis in environmental and civil engineering. Springer.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Razak, S. A., Ramli, N., Azmi, M. I. H. N., Nor, M. I. M., & Noreddie, H. A. H. (2024, September). Fuzzy TOPSIS with ratings based on sub-criteria for selection of supplier. In Proceedings of the 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp. 1–5). IEEE. https://doi.org/10.1109/AiDAS63860.2024.10730581
Saeed, M., Mehmood, A., & Anwar, A. (2021). An extension of TOPSIS based on linguistic terms in triangular intuitionistic fuzzy structure. Punjab University Journal of Mathematics, 53(6), 409–424. https://pujm.pu.edu.pk/index.php/pujm/article/view/277
Saranya, V., Sundari, M., & Priya, S. L. (2024). A comparison of fuzzy and intuitionistic fuzzy frameworks for individual and group replacement approaches with scrap values using centroid-based ranking method for optimal results. Contemporary Mathematics, 5(3), 3676–3688. https://doi.org/10.37256/cm.5320244456
Singh, J., Tyagi, P., Kumar, G., & Agrawal, S. (2020). Convenience store locations prioritization: A fuzzy TOPSIS-GRA hybrid approach. Modern Supply Chain Research and Applications, 2(4), 281–302. https://doi.org/10.1108/MSCRA-01-2020-0001
Sultan, S., & Rosanti, N. (2026). Price, atmosphere, and service quality on customer loyalty. Jurnal Manajemen Bisnis, 13(1), 418–434. https://doi.org/10.33096/jmb.v13i1.1622
Tuğrul, F. (2022). An evaluation of supermarkets from the lens of multiple criteria: The intuitionistic fuzzy TOPSIS method. Black Sea Journal of Engineering and Science, 5(4), 146–150. https://doi.org/10.34248/bsengineering.1155615
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Mathematical Sciences and Informatics

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

