اخبار و اعلانات

 آمار

تعداد نشریات49
تعداد شماره‌ها1,276
تعداد مقالات11,018
تعداد مشاهده مقاله22,699,525
تعداد دریافت فایل اصل مقاله15,331,791
Kaur, Harmandeep, Kaur Sidhu, Sukhpreet. (1405). Comparison of Some MCDM Techniques in a Hesitant Fuzzy Environment. فصلنامه علمی زیست فناوری گیاهان زراعی, 11(2), 33-63. doi: 10.30473/coam.2026.74974.1318
Harmandeep Kaur; Sukhpreet Kaur Sidhu. "Comparison of Some MCDM Techniques in a Hesitant Fuzzy Environment". فصلنامه علمی زیست فناوری گیاهان زراعی, 11, 2, 1405, 33-63. doi: 10.30473/coam.2026.74974.1318
Kaur, Harmandeep, Kaur Sidhu, Sukhpreet. (1405). 'Comparison of Some MCDM Techniques in a Hesitant Fuzzy Environment', فصلنامه علمی زیست فناوری گیاهان زراعی, 11(2), pp. 33-63. doi: 10.30473/coam.2026.74974.1318
Kaur, Harmandeep, Kaur Sidhu, Sukhpreet. Comparison of Some MCDM Techniques in a Hesitant Fuzzy Environment. فصلنامه علمی زیست فناوری گیاهان زراعی, 1405; 11(2): 33-63. doi: 10.30473/coam.2026.74974.1318

Comparison of Some MCDM Techniques in a Hesitant Fuzzy Environment

Control and Optimization in Applied Mathematics
مقاله 3، دوره 11، شماره 2 - شماره پیاپی 22، مرداد 2026، صفحه 33-63
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2026.74974.1318
نویسندگان
Harmandeep Kaur؛ Sukhpreet Kaur Sidhu*
Department of Mathematics, Akal University, Talwandi Sabo (151302), Punjab, India
چکیده
 Multi-criteria decision-making (MCDM) often involves situations characterized by uncertainty, ambiguity, and vagueness. To address such complexities, MCDM techniques play a crucial role. This paper presents a comparative analysis of two widely used methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)—within a hesitant fuzzy environment. Hesitant fuzzy sets allow decision-makers to express hesitation by assigning multiple possible membership values to an element rather than a single value. In this framework, the TOPSIS ranks alternatives based on their closeness to the positive and negative ideal solutions, while the VIKOR identifies a compromise solution by balancing individual and collective regret measures. The effectiveness of the comparison is demonstrated through illustrative numerical examples. Moreover, some real life applications of these methods are discussed.

تازه های تحقیق
  • A systematic and quantitative comparison of TOPSIS and VIKOR is conducted within a Hesitant Fuzzy Sets framework.
  • The analysis moves beyond descriptive evaluation by employing measurable indicators, including rank-difference metrics and Kendall’s Tau, across multiple independent numerical examples.
  • Results show that TOPSIS consistently generates strict and stable rankings, exhibiting lower sensitivity to local deviations in hesitant fuzzy assessments.
  • VIKOR demonstrates higher sensitivity to criterion conflict through its regret-based aggregation, frequently identifying compromise solutions under unstable advantage conditions.
  • Ranking divergence between the two methods increases with greater hesitation dispersion and stronger inter-criterion conflict, with Kendall’s τ decreasing from 1.00 to 0.67 in complex scenarios.
  • The study establishes that method suitability depends on decision-maker priorities: TOPSIS supports decisiveness and clear prioritization, whereas VIKOR facilitates negotiation-oriented and compromise-driven decisions.
  • Practical implications are discussed for applications such as supplier selection, sustainability assessment, policy evaluation, and performance analysis under uncertainty.
  • The findings provide quantitative guidance for selecting appropriate hesitant fuzzy MCDM techniques and lay the groundwork for future hybrid and robustness-oriented extensions.
کلیدواژه‌ها
Multi-criteria decision-making؛ Fuzzy؛ Hesitant fuzzy set؛ TOPSIS؛ VIKOR؛ Kendall’s rank correlation؛ Decision uncertainty
مراجع

[1] Makki, A.A., Abdulaal, R.M.S. (2023). “A hybrid MCDM approach based on fuzzy MEREC-G and fuzzy RATMI”. Mathematics, 11(17), 3773. https://doi.org/ 10.3390/math11173773

[2] Narayanamoorthy, S., Annapoorani, V., Kalaiselvan, S., Kang, D. (2020). “Hybrid hesitant fuzzy multi-criteria decision-making method: A symmetric analysis of the selection of the best water distribution system”. Symmetry, 12(12), 2096.  https: //doi.org/10.3390/sym12122096

[3] 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

[4] Babakordi, F., Allahviranloo, T. (2022). “Introducing and solving the hesitant fuzzy system AX = B”. Control and Cybernetics, 50(4), 553-574. https://doi.org/10. 2478/candc-2021-0031

[5] Babakordi, F., Taghi-Nezhad, N.A. (2021). “Introducing hesitant fuzzy equations and determining market equilibrium price”. Control and Cybernetics, 50(3), 363-382. https://doi.org/10.2478/candc-2021-0022

[6] Babakordi, F., Taghi-Nezhad, N.A. (2024). “Hesitant fuzzy equation”. Control and Optimization in Applied Mathematics, 9(1), 67-79. https://doi.org/10.30473/coam.2023.68208.1240

[7] Babakordi, F. (2024). “Arithmetic operations on generalized trapezoidal hesitant fuzzy numbers and their application to solving generalized trapezoidal hesitant fully fuzzy equation”. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 32(01), 85-108. https://doi.org/10.1142/S0218488524500041

[8] Babakordi, F. (2025). “Introducing two new classes of hesitant fuzzy soft sets”. Fuzzy Optimization and Modeling Journal, 6(1), 1-6. https://doi.org/10.57647/j. fomj.2025.8670

[9] Benayoun, R., Roy, B., Sussman, B. (1966). “ELECTRE: Une m´ethode pour guider le choix en pr´esence de points de vue multiples”. Note de travail 49, SEMA, Paris. https://side.developpement-durable.gouv.fr/CENT/doc/SYRACUSE/296320/electre-une-ethode-pour-guider-le-choix-en-presence-de-points-de-vue-multiples

[10] Bhargava, A.K. (2013). “Fuzzy set theory, fuzzy logic, and their applications”. S. Chand Publishing.

[11] Ferrara, M., Ciano, T., Nava, C.R., Cananà, L. (2023). “Multi-criteria decision analysis: Hesitant fuzzy multi-criteria decision analysis towards expert systems for analyzing financial markets dynamics”. Soft Computing.  https://doi.org/10.1007/ s00500-023-09441-w

[12] Garg, H., Keikha, A., Nehi, H.M. (2020). “Multiple-attribute decision-making problem using TOPSIS and Choquet integral with hesitant fuzzy number information”. Mathematical Problems in Engineering, 2756720.  https://doi.org/10.1155/2020/ 9874951

[13] Hansen, P., Devlin, N.J. (2019). “Multi-criteria decision-analysis (MCDA) in healthcare decision-making”. Oxford Research Encyclopedias, Economics and Finance. https:// api.semanticscholar.org/CorpusID:195451503

[14] Hwang, C.L., Yoon, K. (1981). “Multiple attribute decision making: Methods and applications”. Lecture Notes in Economics and Mathematical Systems, 186, Springer, Berlin. https://doi.org/10.1007/978-3-642-48318-9

[15] Liao, H., Xu, Z. (2013). “A VIKOR-based method for hesitant fuzzy multi-criteria decision making”. Fuzzy Optimization and Decision Making, 12(4), 373-392. https: //doi.org/10.1007/s10700-013-9162-0

[16] Liao H., Xu, Z. (2014). “Subtraction and division operations over hesitant fuzzy sets”. Journal of Intelligent and Fuzzy Systems, 27(1), 65-72. https://doi.org/10.3233/IFS-130978

[17] Liao, H., Xu, Z. (2017). “Hesitant fuzzy decision-making methodologies and applications”. Springer, Berlin, Germany.  https://doi.org/10.1007/ 978-981-10-3265-3

[18] Torra, V., Narukawa, Y. (2009). “On hesitant fuzzy sets and decision”. In: Proceedings of the 8th IEEE International Conference on Fuzzy Systems, Jeju Island, South Korea, 1378- 1382. https://doi.org/10.1109/FUZZY.2009.5276884

[19] 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

[20] Raza, R., Ab Ghani, A.T., Abdullah, L. (2025). “The development of IV-VIKOR method under hesitant fuzzy sets in MCDM”. Communications on Applied Nonlinear Analysis, 32(1s), 212–220. https://doi.org/10.52783/cana.v32.2150

[21] Saaty, T.L. (1980). “The analytic hierarchy process”. McGraw-Hill, New York.

[22] Saha, A., Rage, K., Senapati, T., Chatterjee, P., Zavadskas, E.K., Sliogerienė, J. (2025). “A consensus-based MULTIMOORA framework under probabilistic hesitant fuzzy environment for manufacturing vendor selection”. Informatica, 36(3), 713–736. https: //doi.org/10.15388/24-INFOR581

[23] Taghi-Nezhad, N.A., Babakordi, F. (2023). “Fully hesitant parametric fuzzy equation”. Soft Computing, 27(16), 11099-11110. https://doi.org/10.1007/ s00500-023-08401-8

[24] Torra, V. (2010). “Hesitant fuzzy sets”. International Journal of Intelligent Systems, 25(6), 529-539.  https://doi.org/10.1002/int.20418

[25] Verma, R., Sharma, B.D. (2013). “New operations over hesitant fuzzy sets”. Fuzzy Information and Engineering, 5(2), 129-146. https://doi.org/10.1007/ s12543-013-0137-1

[26] Xu, Z., Xia, M. (2011). “Distance and similarity measures for hesitant fuzzy sets”. Information Sciences, 181(11), 2128-2138. https://doi.org/10.1016/j.ins. 2011.01.028

[27] Zadeh, L.A. (1965). “Fuzzy sets”. Information and Control, 8(3), 338-353. https: //doi.org/10.1016/S0019-9958(65)90241-X

[28] Zhang, N., Wei, G. (2013). “Extension of VIKOR method for decision making problem based on hesitant fuzzy set”. Applied Mathematical Modeling, 37(7), 4938–4947. https://doi.org/10.1016/j.apm.2012.10.002

[29] Zhiming, Z., Wu, C. (2014). “Weighted hesitant fuzzy sets and their application to multicriteria decision making”. Journal of Advances in Mathematics and Computer Science, 4(8), 1091-1123. https://doi.org/10.9734/BJMCS/2014/8533

آمار
تعداد مشاهده مقاله: 86
تعداد دریافت فایل اصل مقاله: 73


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب