Research article Special Issues

An enhanced VIKOR method for multi-criteria group decision-making with complex Fermatean fuzzy sets

  • Received: 01 March 2022 Revised: 04 April 2022 Accepted: 21 April 2022 Published: 16 May 2022
  • This paper aims to propose a new decision-making approach retaining the fascinating traits of the conventional VIKOR method in the context of the enrich multidimensional complex Fermatean fuzzy $ N $-soft set. The VIKOR technique is contemplated as the most reliable decision-making approach among others which employs a strategy to identify the compromise solution with advantageous distance from the positive ideal solution possesses maximum majority utility and minimum individual regret. At the same time, the paramount characteristic of the complex Fermatean fuzzy $ N $-soft set considers the proclivity to capture two-dimensional uncertain and imprecise information along with the multi-valued parameters. This article expands the literature to handle the multi-attribute group decision-making strategy by introducing a technique, namely, the complex Fermatean fuzzy $ N $-soft VIKOR method that amalgamates the unconventional traits of complex Fermatean fuzzy $ N $-soft with the capability of the VIKOR method. The proposed technique permits the assignment of the $ N $-soft grades to the decision-makers, alternatives, and attributes based on their performances. Firstly, we unify these individual opinions of all decision-makers about the alternatives by employing the complex Fermatean fuzzy $ N $-soft weighted average operator. After that, all entities of the aggregated decision matrix are converted into crisp data by utilizing the score function. Furthermore, we calculate the ranking measures of the group utility and the individual regret by assigning the weight of strategy belongs to the interval $ [0, 1]. $ To find the compromise solution, we arrange the ranking measures in ascending order, and the alternative that possesses the conditions of compromise solution is selected. We demonstrate the presented multi-attribute group decision-making technique by selecting the best location for a nuclear power plant. We conduct the comparative analysis of the presented technique with Fermatean fuzzy TOPSIS to endorse the veracity and accuracy of our method. Finally, we explain the merits and limitations of our strategy and give some concluding remarks.

    Citation: Muhammad Akram, G. Muhiuddin, Gustavo Santos-García. An enhanced VIKOR method for multi-criteria group decision-making with complex Fermatean fuzzy sets[J]. Mathematical Biosciences and Engineering, 2022, 19(7): 7201-7231. doi: 10.3934/mbe.2022340

    Related Papers:

  • This paper aims to propose a new decision-making approach retaining the fascinating traits of the conventional VIKOR method in the context of the enrich multidimensional complex Fermatean fuzzy $ N $-soft set. The VIKOR technique is contemplated as the most reliable decision-making approach among others which employs a strategy to identify the compromise solution with advantageous distance from the positive ideal solution possesses maximum majority utility and minimum individual regret. At the same time, the paramount characteristic of the complex Fermatean fuzzy $ N $-soft set considers the proclivity to capture two-dimensional uncertain and imprecise information along with the multi-valued parameters. This article expands the literature to handle the multi-attribute group decision-making strategy by introducing a technique, namely, the complex Fermatean fuzzy $ N $-soft VIKOR method that amalgamates the unconventional traits of complex Fermatean fuzzy $ N $-soft with the capability of the VIKOR method. The proposed technique permits the assignment of the $ N $-soft grades to the decision-makers, alternatives, and attributes based on their performances. Firstly, we unify these individual opinions of all decision-makers about the alternatives by employing the complex Fermatean fuzzy $ N $-soft weighted average operator. After that, all entities of the aggregated decision matrix are converted into crisp data by utilizing the score function. Furthermore, we calculate the ranking measures of the group utility and the individual regret by assigning the weight of strategy belongs to the interval $ [0, 1]. $ To find the compromise solution, we arrange the ranking measures in ascending order, and the alternative that possesses the conditions of compromise solution is selected. We demonstrate the presented multi-attribute group decision-making technique by selecting the best location for a nuclear power plant. We conduct the comparative analysis of the presented technique with Fermatean fuzzy TOPSIS to endorse the veracity and accuracy of our method. Finally, we explain the merits and limitations of our strategy and give some concluding remarks.



    加载中


    [1] S. Opricovic, Multicriteria optimization of civil engineering systems, Faculty of Civil Engineering, Belgrade, 2(1998), 5–21.
    [2] C. L. Hwang, K. Yoon, Methods for multiple attribute decision making, in: Multiple attribute decision making, Lect. Notes Econ. Math. Syst., 186, Springer: Berlin, Germany, 1981. https://doi.org/10.1007/978-3-642-48318-9_3
    [3] T. L. Saaty, Axiomatic foundation of the analytic hierarchy process, Manage. Sci., 32(1986), 841–855. https://doi.org/10.1287/mnsc.32.7.841 doi: 10.1287/mnsc.32.7.841
    [4] J. P. Brans, P. Vincke, B. Mareschal, How to select and how to rank projects: The PROMETHEE method, Eur. J. Oper. Res., 24 (1986), 228–238. https://doi.org/10.1016/0377-2217(86)90044-5 doi: 10.1016/0377-2217(86)90044-5
    [5] P. L. Yu, A class of solutions for group decision problems, Manage. Sci., 19(1973), 936–946. https://doi.org/10.1287/mnsc.19.8.936 doi: 10.1287/mnsc.19.8.936
    [6] M. Zeleny, Multiple Criteria Decision Making, McGraw-Hill, New York, 1982.
    [7] S. Opricovic, G.H. Tzeng, Extended VIKOR method in comparison with outranking methods, Eur. J. Oper. Res., 178(2007), 514–529.
    [8] S. Opricovic, G. H. Tzeng, Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, Eur. J. Oper. Res., 156 (2004), 445–455.
    [9] A. A. Bazzazi, M. Osanloo, B. Karimi, Deriving preference order of open pit mines equipment through MADM methods: Application of modified VIKOR method, Expert Syst. Appl., 38 (2011), 2550–2556. https://doi.org/10.1016/j.eswa.2010.08.043 doi: 10.1016/j.eswa.2010.08.043
    [10] L. A. Zadeh, Fuzzy sets, Inf. Control, 8(1965), 338–353.
    [11] R. E. Bellman, L. A. Zadeh, Decision-making in a fuzzy environment, Manage. Sci., 17(1970), 141–164. https://doi.org/10.1287/mnsc.17.4.B141 doi: 10.1287/mnsc.17.4.B141
    [12] T. C. Wang, T. H. Chang, Fuzzy VIKOR as a resolution for multicriteria group decision-making, in: The 11th International Conference on Industrial Engineering and Engineering Management, (2005), 352–356.
    [13] T. H. Chang, Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan, Inf. Sci., 271(2014), 196–212. https://doi.org/10.1016/j.ins.2014.02.118 doi: 10.1016/j.ins.2014.02.118
    [14] S. Mishra, C. Samantra, S. Datta, S. S. Mahapatra, Multiattribute group decision-making (MAGDM) for supplier selection using fuzzy linguistic modelling integrated with VIKOR method, Int. J. Serv. Oper. Manag., 12(2012), 67–89.
    [15] A. Sanayei, S. F. Mousavi, A. Yazdankhah, Group decision making process for supplier selection with VIKOR under fuzzy environment, Expert Syst. Appl., 37(2010), 24–30. https://doi.org/10.1016/j.eswa.2009.04.063 doi: 10.1016/j.eswa.2009.04.063
    [16] A. Shemshadi, H. Shirazi, M. Toreihi, M. J. Tarokh, A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting, Expert Syst. Appl., 38(10)(2011), 12160–12167. https://doi.org/10.1016/j.eswa.2011.03.027
    [17] S. Opricovic, Fuzzy VIKOR with an application to water resources planning, Expert Syst. Appl., 38(2011), 12983–12990. https://doi.org/10.1016/j.eswa.2011.04.097 doi: 10.1016/j.eswa.2011.04.097
    [18] Y. Ju, A. Wang, Extension of VIKOR method for multicriteria group decision making problem with linguistic information, Appl. Math. Model., 37(2013), 3112–3125. https://doi.org/10.1016/j.apm.2012.07.035 doi: 10.1016/j.apm.2012.07.035
    [19] R. Rostamzadeh, K. Govindan, A. Esmaeili, M. Sabaghi, Application of fuzzy VIKOR for evaluation of green supply chain management practices, Ecol. Indic., 49(2015), 188–203. https://doi.org/10.1016/j.ecolind.2014.09.045 doi: 10.1016/j.ecolind.2014.09.045
    [20] T. C. Wang, J. L. Liang, C. Y. Ho, Multi-criteria decision analysis by using fuzzy VIKOR, in: 2006 International Conference on Service Systems and Service Management, 2(2006), 901–906.
    [21] G. Büyüközkan, D. Ruan, O. Feyzioglu, Evaluating e-learning web site quality in a fuzzy environment, Int. J. Intell. Syst., 22(2007), 567–586. https://doi.org/10.1002/int.20214 doi: 10.1002/int.20214
    [22] O. Taylan, R. Alamoudi, M. Kabli, A. AlJifri, F. Ramzi, E. Herrera-Viedma, Assessment of energy systems using extended fuzzy AHP, fuzzy VIKOR, and TOPSIS approaches to manage non-cooperative opinions, Sustainability, 12(2020), 2745. https://doi.org/10.3390/su12072745 doi: 10.3390/su12072745
    [23] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst., 20(1986), 87–96. https://doi.org/10.1007/978-3-7908-1870-3_1
    [24] P. Gupta, M. K. Mehlawat N. Grover, Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based on a new extended VIKOR method, Inf. Sci., 370(2016), 184–203. https://doi.org/10.1016/j.ins.2016.07.058 doi: 10.1016/j.ins.2016.07.058
    [25] J. Hu, X. Zhang, Y. Yang, Y. Liu, X. Chen, New doctors ranking system based on VIKOR method, Int. Trans. Oper. Res., 27(2020), 1236–1261. https://doi.org/10.1111/itor.12569 doi: 10.1111/itor.12569
    [26] S. M. Mousavi, B. Vahdani, S. S. Behzadi, Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems, Iran. J. Fuzzy Syst., 13(2016), 45–65. https://doi.org/10.22111/IJFS.2016.2286 doi: 10.22111/IJFS.2016.2286
    [27] S. P. Wan, Q. Y. Wang, J. Y. Dong, The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers, Knowl. Based. Syst., 52(2013), 65–77. https://doi.org/10.1016/j.knosys.2013.06.019 doi: 10.1016/j.knosys.2013.06.019
    [28] R. R. Yager, Pythagorean fuzzy subsets, in: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), (2013), 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
    [29] R. R. Yager, Pythagorean membership grades in multicriteria decision making, IEEE Trans. Fuzzy Syst., 22(2013), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989 doi: 10.1109/TFUZZ.2013.2278989
    [30] F. B. Cui, X. Y. You, H. Shi, H. C. Liu, Optimal siting of electric vehicle charging stations using Pythagorean fuzzy VIKOR approach, Math. Probl. Eng., 2018(2018), Article ID 9262067. https://doi.org/10.1155/2018/9262067 doi: 10.1155/2018/9262067
    [31] M. Gul, M. F. Ak, A. F. Guneri, Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry, J. Saf. Res., 69(2019), 135–153. https://doi.org/10.1016/j.jsr.2019.03.005 doi: 10.1016/j.jsr.2019.03.005
    [32] R. R. Yager, Generalized orthopair fuzzy sets, IEEE Trans. Fuzzy Syst., 25(2016), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005
    [33] T. Senapati, R. R. Yager, Fermatean fuzzy sets, J. Ambient. Intell. Humaniz. Comput., 11(2020), 663–674. https://doi.org/10.1007/s12652-019-01377-0
    [34] M. K. Ghorabaee, M. Amiri, M. H. Tabatabaei, E. K. Zavadskas, A. Kaklauskas, A new decision-making approach based on Fermatean fuzzy sets and WASPAS for green construction supplier evaluation, Mathematics, 8(2020), 2202. https://doi.org/10.3390/math8122202 doi: 10.3390/math8122202
    [35] D. Liu, Y. Liu, X. Chen, Fermatean fuzzy linguistic set and its application in multicriteria decision making, Int. J. Intell. Syst., 34(2019), 878–894. https://doi.org/10.1002/int.22079 doi: 10.1002/int.22079
    [36] H. Garg, G. Shahzadi, M. Akram, Decision-making analysis based on Fermatean fuzzy Yager aggregation operators with application in COVID-19 testing facility, Math. Probl. Eng., 2020(2020), Article ID 7279027. https://doi.org/10.1155/2020/7279027 doi: 10.1155/2020/7279027
    [37] T. Y. Chen, The likelihood-based optimization ordering model for multiple criteria group decision making with Pythagorean fuzzy uncertainty, Neural Comput. Appl., 33 (2021), 4865–4900. https://doi.org/10.1007/s00521-020-05278-8 doi: 10.1007/s00521-020-05278-8
    [38] P. A. Ejegwa, Modified Zhang and Xu's distance measure for Pythagorean fuzzy sets and its application to pattern recognition problems, Neural Comput. Appl., 32(2020), 10199-10208. https://doi.org/10.1007/s00521-019-04554-6 doi: 10.1007/s00521-019-04554-6
    [39] F. Feng, H. Fujita, M.T. Ali, R.R. Yager, X. Liu, Another view on generalized intuitionistic fuzzy soft sets and related multiattribute decision making methods, IEEE Trans. Fuzzy Syst., 27(2019), 474–488. https://doi.org/10.1109/TFUZZ.2018.2860967. doi: 10.1109/TFUZZ.2018.2860967
    [40] Y. Han, Y. Deng, Z. Cao, C.T. Lin, An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making, Neural Comput. Appl., 32(2020), 7641–7659. https://doi.org/10.1007/s00521-019-04014-1 doi: 10.1007/s00521-019-04014-1
    [41] R. Krishankumar, K. S. Ravichandran, V. Shyam, S. V. Sneha, S. Kar, H. Garg, Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information, Neural Comput. Appl., 32(2020), 14031–14045. https://doi.org/10.1007/s00521-020-04802-0 doi: 10.1007/s00521-020-04802-0
    [42] F. Zhou, T. Y. Chen, An extended Pythagorean fuzzy VIKOR method with risk preference and a novel generalized distance measure for multicriteria decision-making problems, Neural Comput. Appl., (2021), 1–24. https://doi.org/10.1007/s00521-021-05829-7
    [43] C. N. Wang, C. C. Su, V.T. Nguyen, Nuclear power plant location selection in Vietnam under fuzzy environment conditions, Symmetry, 10(11)(2018), 548. https://doi.org/10.3390/sym10110548
    [44] Shumaiza, M. Akram, A. N. Al-Kenani, J. C. R. Alcantud, Group decision-making based on the VIKOR method with Trapezoidal bipolar fuzzy information, Symmetry, 11(2019), 1313. https://doi.org/10.3390/sym11101313 doi: 10.3390/sym11101313
    [45] D. Ramot, R. Milo, M. Friedman, A. Kandel, Complex fuzzy sets, IEEE Trans. Fuzzy Syst., 10(2002), 171–186. https://doi.org/10.1109/91.995119
    [46] D. E. Tamir, L. Jin, A. Kandel, A new interpretation of complex membership grade, Int. J. Intell. Syst., 26(2011), 285–312. https://doi.org/10.1002/int.20454 doi: 10.1002/int.20454
    [47] A. M. Alkouri, A. R. Salleh, Complex intuitionistic fuzzy sets, AIP Conference Proceedings, 1482(2012), 464–470. https://doi.org/10.1063/1.4757515
    [48] K. Ullah, T. Mahmood, Z. Ali, N. Jan, On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition, Complex Intell. Syst., 6(2020), 15–27. https://doi.org/10.1007/s40747-019-0103-6 doi: 10.1007/s40747-019-0103-6
    [49] X. Ma, M. Akram, K. Zahid, J. C. R. Alcantud, Group decision-making framework using complex Pythagorean fuzzy information, Neural Comput. Appl., 33(2021), 2085–2105. https://doi.org/10.1007/s00521-020-05100-5 doi: 10.1007/s00521-020-05100-5
    [50] M. Akram, C. Kahraman, K. Zahid, Group decision-making based on complex spherical fuzzy VIKOR approach, Knowl. Based. Syst., 216(2021), 106793. https://doi.org/10.1016/j.knosys.2021.106793 doi: 10.1016/j.knosys.2021.106793
    [51] Z. Pawlak, Rough sets, Int. J. Comput. Inf. Sci., 11(1982), 341–356. https://doi.org/10.1007/BF01001956
    [52] D.A. Molodtsov, Soft set theory - First results, Comput. Math. Appl., 37(1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
    [53] D. A. Molodtsov, The theory of soft sets, URSS Publishers Moscow (in Russian), 2004.
    [54] P. K. Maji, R. Biswas, A. R. Roy, Intuitionistic fuzzy soft sets, J. Fuzzy Math., 9(2001), 677–692.
    [55] X. Peng, Y. Yang, J. Song, Pythagorean fuzzy soft set and its application, Computer Engineering, 41(2015), 224–229.
    [56] G. Shahzadi, M. Akram, Group decision-making for the selection of an antivirus mask under Fermatean fuzzy soft information, J. Intell. Fuzzy Syst., 40(2020), 1–6. https://doi.org/10.3233/JIFS-201760 doi: 10.3233/JIFS-201760
    [57] V. Salsabeela, S. J. John, TOPSIS techniques on Fermatean fuzzy soft sets, AIP Conference Proceedings, 2336(2021), 040022. https://doi.org/10.1063/5.0045914
    [58] P. Thirunavukarasu, R. Suresh, V. Ashokkumar, Theory of complex fuzzy soft set and its applications, Int. J. Eng. Sci. Tech., 3(2017), 13–18.
    [59] T. Kumar, R. K. Bajaj, On complex intuitionistic fuzzy soft sets with distance measures and entropies, J. Math., 2014(2014), Article ID 972198. https://doi.org/10.1155/2014/972198 doi: 10.1155/2014/972198
    [60] J. C. R. Alcantud, F. Feng, R. R. Yager, An $N$-soft set approach to rough sets, IEEE Trans. Fuzzy Syst., 28(2020), 2996–3007. https://doi.org/10.1109/TFUZZ.2019.2946526. doi: 10.1109/TFUZZ.2019.2946526
    [61] F. Fatimah, D. Rosadi, R. B. F. Hakim, J. C. R. Alcantud, $N$-soft sets and their decision making algorithms, Soft Comput., 22(4)(2018), 3829–3842. https://doi.org/10.1007/s00500-017-2838-6
    [62] M. Akram, A. Adeel, J.C.R. Alcantud, Fuzzy $N$-soft sets: A novel model with applications, J. Intell. Fuzzy Syst., 35 (2018), 4757–4771. https://doi.org/10.3233/JIFS-18244 doi: 10.3233/JIFS-18244
    [63] M. Akram, A. Adeel, J. C. R. Alcantud, Group decision making methods based on hesitant $N$-soft sets, Expert Syst. Appl., 115(2019), 95–105. https://doi.org/10.1016/j.eswa.2018.07.060 doi: 10.1016/j.eswa.2018.07.060
    [64] M. Akram, A. Adeel, J. C. R. Alcantud, Hesitant fuzzy $N$-soft sets: A new model with applications in decision-making, J. Intell. Fuzzy Syst., 36(2019), 6113–6127. https://doi.org/10.3233/JIFS-181972 doi: 10.3233/JIFS-181972
    [65] M. Akram, G. Ali, J. C. R. Alcantud, New decision-making hybrid model: Intuitionistic fuzzy $N$-soft rough sets, Soft Comput., 23(2019), 9853–9868. https://doi.org/10.1007/s00500-019-03903-w doi: 10.1007/s00500-019-03903-w
    [66] H. Zhang, D. Jia-hua, C. Yan, Multi-attribute group decision-making methods based on Pythagorean fuzzy $N$-soft sets, IEEE Access, 8(2020), 62298–62309. https://doi.org/10.1109/ACCESS.2020.2984583. doi: 10.1109/ACCESS.2020.2984583
    [67] M. Akram, F. Wasim, A. N. Al-Kenani, A hybrid decision-making approach under complex Pythagorean fuzzy $N$-soft sets, Int. J. Comput. Intell. Syst., 14(2021), 1263–1291. https://doi.org/10.2991/ijcis.d.210331.002 doi: 10.2991/ijcis.d.210331.002
    [68] M. Akram, M. Shabir, A. N. Al-Kenani, J. C. R. Alcantud, Hybrid decision-making frameworks under complex spherical fuzzy $N$-soft sets, J. Math., 2021(2021), Article ID 5563215. https://doi.org/10.1155/2021/5563215 doi: 10.1155/2021/5563215
    [69] M. Akram, U. Amjad, J. C. R. Alcantud, G. Santos-García, Complex Fermatean fuzzy $N$-soft sets: A new hybrid model with applications, J. Ambient. Intell. Humaniz. Comput. (2022). https://doi.org/10.1007/s12652-021-03629-4
    [70] F. Fatimah, J. C. R. Alcantud, The multi-fuzzy $N$-soft set and its applications to decision-making, Neural Comput. Appl., (2021), 1–10. https://doi.org/10.1007/s00521-020-05647-3
    [71] J. Zhang, G. Kou, Y. Peng, Y. Zhang, Estimating priorities from relative deviations in pairwise comparison matrices, Inf. Sci., 552(2021), 310–327. https://doi.org/10.1016/j.ins.2020.12.008 doi: 10.1016/j.ins.2020.12.008
    [72] G. Li, G. Kou, Y. Peng, A group decision making model for integrating heterogeneous information, IEEE Trans. Syst. Man Cybern. Syst., 48(2016), 982–992. https://doi.org/10.1109/TSMC.2016.2627050 doi: 10.1109/TSMC.2016.2627050
    [73] H. Zhang, G. Kou, Y. Peng, Soft consensus cost models for group decision making and economic interpretations, Eur. J. Oper. Res., 277(2019), 964–980. https://doi.org/10.1016/j.ejor.2019.03.009 doi: 10.1016/j.ejor.2019.03.009
    [74] G. Kou, Ö. Olgu Akdeniz, H. Dinçer and S. Y$\ddot{u}$ksel, Fintech investments in European banks: A hybrid IT2 fuzzy multidimensional decision-making approach, Financial Innov., 7(2021), 1–28. https://doi.org/10.1186/s40854-021-00256-y doi: 10.1186/s40854-021-00256-y
    [75] S. Gül, Fermatean fuzzy set extensions of SAW, ARAS, and VIKOR with applications in COVID-19 testing laboratory selection problem, Expert Syst., 38(2021), e12769. https://doi.org/10.1111/exsy.12769 doi: 10.1111/exsy.12769
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2093) PDF downloads(145) Cited by(11)

Article outline

Figures and Tables

Figures(5)  /  Tables(19)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog