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The method of judging satisfactory consistency of linguistic judgment matrix based on adjacency matrix and 3-loop matrix

  • Received: 10 January 2024 Revised: 29 April 2024 Accepted: 13 May 2024 Published: 05 June 2024
  • MSC : 15, 94D05

  • Language phrases are an effective way to express uncertain pieces of information, and easily conforms to the language habits of decision makers to describe the evaluation of things. The consistency judgment of a linguistic judgment matrices is the key to analytic hierarchy process (AHP). If a linguistic judgment matrix has a satisfactory consistency, then the rank of the decision schemes can be determined. In this study, the comparison relation between the decision schemes is first represented by a directed graph. The preference relation matrix of the linguistic judgment matrix is the adjacency matrix of the directed graph. We can use the $ n - 1 $ st power of the preference relation to judge the linguistic judgment matrix whether has a satisfactory consistency. The method is utilized if there is one and only one element in the $ n - 1 $ st power of the preference relation, and the element 1 is not on the main diagonal. Then the linguistic judgment matrix has a satisfactory consistency. If there are illogical judgments, the decision schemes that form a 3-loop can be identified and expressed through the second-order sub-matrix of the preference relation matrix. The feasibility of this theory can be verified through examples. The corresponding schemes for illogical judgments are represented in spatial coordinate system.

    Citation: Fengxia Jin, Feng Wang, Kun Zhao, Huatao Chen, Juan L.G. Guirao. The method of judging satisfactory consistency of linguistic judgment matrix based on adjacency matrix and 3-loop matrix[J]. AIMS Mathematics, 2024, 9(7): 18944-18967. doi: 10.3934/math.2024922

    Related Papers:

  • Language phrases are an effective way to express uncertain pieces of information, and easily conforms to the language habits of decision makers to describe the evaluation of things. The consistency judgment of a linguistic judgment matrices is the key to analytic hierarchy process (AHP). If a linguistic judgment matrix has a satisfactory consistency, then the rank of the decision schemes can be determined. In this study, the comparison relation between the decision schemes is first represented by a directed graph. The preference relation matrix of the linguistic judgment matrix is the adjacency matrix of the directed graph. We can use the $ n - 1 $ st power of the preference relation to judge the linguistic judgment matrix whether has a satisfactory consistency. The method is utilized if there is one and only one element in the $ n - 1 $ st power of the preference relation, and the element 1 is not on the main diagonal. Then the linguistic judgment matrix has a satisfactory consistency. If there are illogical judgments, the decision schemes that form a 3-loop can be identified and expressed through the second-order sub-matrix of the preference relation matrix. The feasibility of this theory can be verified through examples. The corresponding schemes for illogical judgments are represented in spatial coordinate system.



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    [1] L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [2] V. Torra, Hesitant fuzzy sets, Int. J. Intell. Syst, 25 (2010), 529–539. https://doi.org/10.1002/int.20418 doi: 10.1002/int.20418
    [3] Q. Pang, H. Wang, Z. S. Xu, Probabilistic linguistic term sets in multi-attribute group decision making, Inform. Sci., 369 (2016), 128–143. https://doi.org/10.1016/j.ins.2016.06.021 doi: 10.1016/j.ins.2016.06.021
    [4] Z. S. Xu, Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment, Inform. Sci., 168 (2004), 171–184. https://doi.org/10.1016/j.ins.2004.02.003 doi: 10.1016/j.ins.2004.02.003
    [5] R. M. Rodriguez, L. Martinez, F. Herrera. Hesitant fuzzy linguistic term sets for decision making, IEEE T. Fuzzy Syst., 20 (2012), 109–119. https://doi.org/10.1109/tfuzz.2011.2170076 doi: 10.1109/tfuzz.2011.2170076
    [6] L. A. Zadeh, A fuzzy-set-theoretic interpretation of linguistic hedges, J. Cybernet., 2 (1972), 4–34. https://doi.org/10.1080/01969727208542910 doi: 10.1080/01969727208542910
    [7] Y. Y. Lian, Y. B. Jua, J. D. Qin, W. Pedryczc, Multi-granular linguistic distribution evidential reasoning method for renewable energy project risk assessment, Inform. Fusion, 65 (2021), 147–164. https://doi.org/10.1016/j.inffus.2020.08.010 doi: 10.1016/j.inffus.2020.08.010
    [8] Z. S. Chen, Y. Yang, X. J. Wang, K. S. Chin, K. L. Sui, Fostering linguistic decision-making under uncertainty: aportional interval type-2 hesitant fuzzy TOPSIS approach based on Hamacher aggregation operators and optimization models, Inform. Sci., 500 (2019), 229–258. https://doi.org/10.1016/j.ins.2019.05.074 doi: 10.1016/j.ins.2019.05.074
    [9] S. Zhang, J. Zhu, X. Liu, Y. Chen, Z. Ma, Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation, Appl. Soft. Comput., 60 (2017), 30–47. https://doi.org/10.1016/j.asoc.2017.06.028 doi: 10.1016/j.asoc.2017.06.028
    [10] Y. C. Dong, Y. Z. Wu, H. J. Zhang, G. Q. Zhang, Multi-granular unbalanced linguistic distribution assessments with interval symbolic proportions, Knowl. Based Sys., 82 (2015), 139–151. https://doi.org/10.1016/j.knosys.2015.03.003 doi: 10.1016/j.knosys.2015.03.003
    [11] Y. Lin, Y. M. Wang, Prioritization of hesitant multiplicative preference relations based on data envelopment analysis for group decision making, Neural. Comput. Appl., 31 (2019), 437–447. https://doi.org/10.1007/s00521-017-3075 doi: 10.1007/s00521-017-3075
    [12] F. F. Jin, M. Cao, J. P. Liu, L. Martinez, H. Y. Chen, Consistency and trust relationship-driven social network group decision-making method with probabilistic linguistic information, Appl. Soft. Comput., 103 (2021), 107170. https://doi.org/10.1016/j.asoc.2021.107170 doi: 10.1016/j.asoc.2021.107170
    [13] Z. L. Wang, Y. M. Wang, Prospect theory-based group decision-making with stochastic uncertainty and 2-tuple aspirations under linguistic assessments, Inform. Fusion, 56 (2020), 81–92. https://doi.org/10.1016/j.inffus.2019.10.001 doi: 10.1016/j.inffus.2019.10.001
    [14] X. J. Gou, Z. S. Xu, H. C. Liao, Group decision making with compatibility measures of hesitant fuzzy linguistic preference relations, Soft Comput., 23 (2019), 1511–1527. https://doi.org/10.1007/s00500-017-2871-5 doi: 10.1007/s00500-017-2871-5
    [15] P. Grogelj, L. Z. Stirn, Acceptable consistency of aggregated comparison matrices in analytic hierarchy process, Eur. J. Oper. Res., 223 (2012), 417–420. https://doi.org/10.1016/j.ejor.2012.06.016 doi: 10.1016/j.ejor.2012.06.016
    [16] J. Hu, L. Pan, Y. Yang, H. Chen, A group medical diagnosis model based on intuitionistic fuzzy soft sets, Appl. Soft Comput., 77 (2019), 453–466. https://doi.org/10.1016/j.asoc.2019.01.041 doi: 10.1016/j.asoc.2019.01.041
    [17] S. H. Wu, X. D. Liu, Z. X. Li, Y. Zhou, A consistency improving method in the analytic hierarchy process based on directed circuit analysis, J. Syst. Eng. Elect., 30 (2019), 1160–1181. https://doi.org/10.21629/jsee.2019.06.11 doi: 10.21629/jsee.2019.06.11
    [18] J. A. Morente-Molinera, X. Wu, A. Morfeq, R. Al-Hmouz, E. Herrera-Viedma, A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures, Inform. Fusion, 53 (2020), 240–250. https://doi.org/10.1016/j.inffus.2019.06.028 doi: 10.1016/j.inffus.2019.06.028
    [19] Y. Zhang, H. X. Ma, B. H. Liu, Group decision making with 2-tuple intuitionistic fuzzy linguistic preference relations. Soft. Comput, 16 (2012), 1439–1446. https://doi.org/10.1007/s00500-012-0847-z doi: 10.1007/s00500-012-0847-z
    [20] C. Li, Y. Gao, Y. Dong, Managing ignorance elements and personalized individual semantics under incomplete linguistic distribution context in group decision making, Group. Decis. Negot., 30 (2021), 97–118. https://doi.org/10.1007/s10726-020-09708-9 doi: 10.1007/s10726-020-09708-9
    [21] A. R. Mishra, P. Rani, Interval-valued intuitionistic fuzzy WASPAS method: application in reservoir flood control management policy, Group. Decis. Negot., 30 (2018), 1047–1078. https://doi.org/10.1007/s10726-018-9593-7 doi: 10.1007/s10726-018-9593-7
    [22] S. C. Su, T. C. Wang, Solving muti-criteria decision making with incomplete linguistic preference relations, Expert. Syst. Appl., 38 (2011), 10882–10888. https://doi.org/10.1016/j.eswa.2011.02.123 doi: 10.1016/j.eswa.2011.02.123
    [23] P. Rani, COPRAS method based on interval-valued hesitant Fermatean fuzzy sets and its application in selecting desalination technology, Appl. Soft Comput., 119 (2022), 108570. https://doi.org/10.1016/j.asoc.2022.108570 doi: 10.1016/j.asoc.2022.108570
    [24] M. Deveci, S. C. Öner, Muharrem, E. Ciftci, E. Özcan, D. Pamucar, Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection, Appl. Soft Comput., 114 (2022), 108076. https://doi.org/10.1016/j.asoc.2021.108076 doi: 10.1016/j.asoc.2021.108076
    [25] M. Deveci, V. Simic, S. Karagoz, J. Antucheviciene, An interval type-2 fuzzy sets based Delphi approach to evaluate site selection indicators of sustainable vehicle shredding facilities, Appl. Soft Comput., 118 (2022), 108465. https://doi.org/10.1016/j.asoc.2022.108465 doi: 10.1016/j.asoc.2022.108465
    [26] L. Wang, H. Wang, An integrated qualitative group decision-making method for assessing health-care waste treatment technologies based on linguistic terms with weakened hedges, Appl. Soft Comput., 117 (2022), 108435. https://doi.org/10.1016/j.asoc.2022.108435 doi: 10.1016/j.asoc.2022.108435
    [27] X. L. Wu, H. C. Liao, Geometric linguistic scale and its application in multi-attribute decision-making for green agricultural product supplier selection, Fuzzy. Set. Syst., 458 (2023), 182–200. https://doi.org/10.1016/j.fss.2022.08.026 doi: 10.1016/j.fss.2022.08.026
    [28] Q. Yang, Z. S. Chen, Catherine, Y. P. Chan, W. Pedrycz, L. Martínez, et al., Large-scale group decision-making for prioritizing engineering characteristics in quality function deployment under comparative linguistic environment, Appl. Soft Comput., 127 (2022), 109359. https://doi.org/10.1016/j.asoc.2022.109359 doi: 10.1016/j.asoc.2022.109359
    [29] G. Strauch, W. Finger, F. Rodrigues, L. Junior, A hesitant fuzzy linguistic QFD approach for formulating sustainable supplier development programs, Int. J. Prod. Econ., 247 (2022), 108428. https://doi.org/10.1016/j.ijpe.2022.108428 doi: 10.1016/j.ijpe.2022.108428
    [30] X. Tan, J. J. Zhu, I. Palomares, X. Liu, On consensus reaching process based on social network analysis in uncertain linguistic group decision making: Exploring limited trust propagation and preference modification attitudes, Inform. Fusion, 78 (2022), 180–198. https://doi.org/10.1016/j.i-nffus.2021.09.006 doi: 10.1016/j.i-nffus.2021.09.006
    [31] S. Siraj, L. Mikhailov, J. Keane, A heuristic method to rectify intransitive judgments in pairwise comparison matrices, Eur. J. Oper. Res., 216 (2012), 420–428. https://doi.org/10.1016/j.ejor.2011.07.034 doi: 10.1016/j.ejor.2011.07.034
    [32] Z. S. Xu, Incomplete complementary judgment matrix, in Chinese, Syst. Eng.Theory Pract., 24 (2004), 91–97.
    [33] Z. P. Fan, S. H Xiao, The consistency and ranking method for comparison matrix with linguistic assessment, in Chinese, Syst. Eng.Theory Pract., 22 (2002), 87–91.
    [34] Z. S. Xu, C. P. Wei. A consistency improving method in the analytic hierarchy process. Eur. J. Oper. Res, 116 (1999), 443–449. https://doi.org/10.1016/S0377-2217(98)00109-X doi: 10.1016/S0377-2217(98)00109-X
    [35] D. Cao, L.C. Leueg, J. S. Law, Modifying inconsistent comparison matrix in analytic hierarchy process: a heuristic approach, Decis. Support. Syst., 44 (2008), 944–953. https://doi.org/10.1016/j.dss.2007.11.002 doi: 10.1016/j.dss.2007.11.002
    [36] Q. Zhang, T. Huang, X. Tang, K. Xu, Witold Pedrycz e A linguistic information granulation model and its penalty function-based co-evolutionary PSO solution approach for supporting GDM with distributed linguistic preference relations, Inform. Fusion, 77 (2021), 118–132. https://doi.org/10.1016/j.inffus.2021.07.017 doi: 10.1016/j.inffus.2021.07.017
    [37] M. Delgado, F. Herrera, E. Herrera-Viedma, L. Martínez, Combining numerical and linguistic information in group decision making, Inform. Sci., 107 (1998), 177–194. https://doi.org/10.1016/S0020-0255(97)10044-5 doi: 10.1016/S0020-0255(97)10044-5
    [38] G. Bordogna, M. Fedrizzi, G. Pasi, A linguistic modeling of consensus in group decision making based on OWA operators, IEEE. T. Syst. Man. Cy. A, 27 (1997), 126–132. https://doi.org/10.1109/3468.553232 doi: 10.1109/3468.553232
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