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Multi-attribute decision-making method with triangular fuzzy numbers based on regret theory and the catastrophe progression method

  • Academic editor: Sheldon Wang
  • Received: 25 May 2022 Revised: 12 July 2022 Accepted: 14 July 2022 Published: 18 August 2022
  • The purpose of this paper was to develop a novel triangular fuzzy method for multi-attribute decision-making to eliminate the influence of indicator weights on scheme selection and account for the regret psychology of decision-makers. Therefore, considering the consequences of regret aversion and subjective weighting, we propose a multi-attribute decision-making method with triangular fuzzy number based on regret theory and catastrophe progression. First, to eliminate the influence of various dimensions on the decision-making results, the decision matrix is described by a triangular fuzzy number, and the regret value matrix and rejoicing value matrix are independently constructed by applying regret theory. Second, the importance ranking of attributes is improved to eliminate the influence of subjective weighting by employing the maximizing deviation method; and the comprehensive catastrophe progression attribute is calculated to rank the alternatives. Finally, an instance of investment project selection is provided to prove the availability and superiority. In conclusion, the proposed method not only considers decision-makers' bounded rationality for decision-making, but it also expands the application of catastrophe progression methods under the condition of a triangular fuzzy environment.

    Citation: Nian Zhang, Yifan Zhou, Qiang Pan, Guiwu Wei. Multi-attribute decision-making method with triangular fuzzy numbers based on regret theory and the catastrophe progression method[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12013-12030. doi: 10.3934/mbe.2022559

    Related Papers:

  • The purpose of this paper was to develop a novel triangular fuzzy method for multi-attribute decision-making to eliminate the influence of indicator weights on scheme selection and account for the regret psychology of decision-makers. Therefore, considering the consequences of regret aversion and subjective weighting, we propose a multi-attribute decision-making method with triangular fuzzy number based on regret theory and catastrophe progression. First, to eliminate the influence of various dimensions on the decision-making results, the decision matrix is described by a triangular fuzzy number, and the regret value matrix and rejoicing value matrix are independently constructed by applying regret theory. Second, the importance ranking of attributes is improved to eliminate the influence of subjective weighting by employing the maximizing deviation method; and the comprehensive catastrophe progression attribute is calculated to rank the alternatives. Finally, an instance of investment project selection is provided to prove the availability and superiority. In conclusion, the proposed method not only considers decision-makers' bounded rationality for decision-making, but it also expands the application of catastrophe progression methods under the condition of a triangular fuzzy environment.



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    [1] K. Y. Wang, Township recycling performance assessment based on fuzzy TOPSIS model: An empirical investigation using the artificial intelligence-based VIKOR approach, J. Intell. Fuzzy Syst., 40 (2021), 8523–8529. https://doi.org/10.3233/JIFS-189672 doi: 10.3233/JIFS-189672
    [2] C. Karamaşa, D. Karabasevic, D. Stanujkic, An extended single-valued neutrosophic AHP and MULTIMOORA method to evaluate the optimal training aircraft for flight training organizations, Facta Univ. Ser. Mechan. Eng., 19 (2021), 555–578. https://doi.org/10.22190/FUME210521059K doi: 10.22190/FUME210521059K
    [3] R. Sahu, S. R. Dash, S. Das, Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory, Decis. Making Appl. Manage. Eng., 4 (2021), 555–578. https://doi.org/10.31181/dmame2104104s doi: 10.31181/dmame2104104s
    [4] S. Guo, S. Liu, Z. Fang, Multi-attribute decision making model based on kernel and degree of greyness of interval grey numbers, Control Decis., 31 (2016), 1042–1046. https://doi.org/10.13195/j.kzyjc.2015.0633 doi: 10.13195/j.kzyjc.2015.0633
    [5] L. Xu, Z. Shu, C. Pang, Interval multi-attribute decision making method based on pass value adaptive regret theory and evidence theory, J. Syst. Sci. Math. Sci., 39 (2019), 857–874.
    [6] M. Lin, H. Wang, Z. Xu, TODIM-based multi-criteria decision-making method with hesitant fuzzy linguistic term sets, Artif. Intell. Rev., 53 (2020), 3647–3671. https://doi.org/10.1007/s10462-019-09774-9 doi: 10.1007/s10462-019-09774-9
    [7] M. Lin, Z. Chen, Z. Xu, X. Gou, F. Herrera, Score function based on concentration degree for probabilistic linguistic term sets: an application to TOPSIS and VIKOR, Inf. Sci., 551 (2021), 270–290. https://doi.org/10.1016/j.ins.2020.10.061 doi: 10.1016/j.ins.2020.10.061
    [8] K. Yin, B. Yang, X. Jin, Grey fuzzy multiple attribute group decision-making methods based on interval grey triangular fuzzy numbers partitioned bonferroni mean, Symmetry, 12 (2020), 628. https://doi.org/10.3390/sym12040628 doi: 10.3390/sym12040628
    [9] C. Tan, X. Zhang. VIKOR method for uncertain risky multi-attribute decision making based on regret theory, Stat. Decis., 35 (2019), 47–51.
    [10] P. Biswas, S. Pramanik, B. C. Giri, Aggregation of triangular fuzzy neutrosophic set information and its application to multi-attribute decision making, Neutrosophic Syst., 12 (2016), 20–40.
    [11] Z. Huang, J. Luo, Similarity programming model for triangular fuzzy number-based uncertain multi-attribute decision making and its application, Syst. Eng. Electron., 38 (2016), 1100–1106.
    [12] J. Dong, S. Wan, S. Chen, Fuzzy best-worst method based on triangular fuzzy numbers for multi-criteria decision-making, Inf. Sci., 547 (2021), 1080–1104. https://doi.org/10.1016/j.ins.2020.09.014 doi: 10.1016/j.ins.2020.09.014
    [13] J. Wang, N. Ye, L. Ge, Steady-state power quality synthetic evaluation based on the triangular fuzzy BW method and interval VIKOR method, Appl. Sci., 10 (2020), 28–39. https://doi.org/10.3390/app10082839 doi: 10.3390/app10082839
    [14] F. Wang, Preference degree of triangular fuzzy numbers and its application to multi-attribute group decision making, Expert Syst. Appl., 178 (2021), 114982. https://doi.org/10.1016/j.eswa.2021.114982 doi: 10.1016/j.eswa.2021.114982
    [15] M. Małecka, The normative decision theory in economics: a philosophy of science perspective. The case of the expected utility theory, J. Econ. Methodol., 27 (2020), 36–50. https://doi.org/10.1080/1350178X.2019.1640891 doi: 10.1080/1350178X.2019.1640891
    [16] M. Wei, Random expected utility theory with a continuum of prizes, Ann. Oper. Res., 271 (2018), 787–809. https://doi.org/10.1007/s10479-018-2914-z doi: 10.1007/s10479-018-2914-z
    [17] J. Han, S. Ye, J. Chai, J. Li, Case-based decision analysis method based on regret theory for hybrid multiple attributes decision making, Chin. J. Manage. Sci., 24 (2016), 108–116.
    [18] Y. Wang, J. Wang, T. Wang, Fuzzy stochastic multi-criteria decision-making methods with interval neutrosophic probability based on regret theory, J. Intell. Fuzzy Syst., 35 (2018), 2309–2322. https://doi.org/10.3233/JIFS-17622 doi: 10.3233/JIFS-17622
    [19] X. Xu, J. Xie, N. Yue, H. Wang, Probabilistic uncertain linguistic TODIM method based on the generalized Choquet integral and its application, Int. J. Intell. Comput. Cybern., 14 (2021), 122–144. https://doi.org/10.1108/IJICC-09-2020-0108 doi: 10.1108/IJICC-09-2020-0108
    [20] D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk, in Handbook Fundamentals Financial Decision Making: Part I, (2013), 99–127. https://doi.org/10.1142/9789814417358_0006
    [21] A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty, J. Risk Uncertainty, 5 (1992), 297–323. https://doi.org/10.1007/BF00122574 doi: 10.1007/BF00122574
    [22] L. Chen, N. Luo, Pythagorean fuzzy multi-criteria decision-making based on prospect theory, Syst. Eng. Theory Pract., 40 (2020), 726–735. https://doi.org/10.12011/1000-6788-2018-2422-10 doi: 10.12011/1000-6788-2018-2422-10
    [23] J. Yang, Y. Fang, S. Du, Evolutionary game analysis of cooperative innovation based on reference dependence, Chin. J. Manage. Sci., 28 (2020), 191–200.
    [24] T. Ning, X. Wang, X. Hu, Study on disruption management strategy of terminal logistics based on prospect theory, Syst. Eng. Theory Pract., 39 (2019), 673–681.
    [25] X. Bao, G. Cao, X. Xing, P. Wang, Reason of real earnings management:An explanation of prospect theory, J. Ind. Eng. Eng. Manage., 31 (2017), 45–51.
    [26] M. Lin, X. Li, L. Chen, Linguistic qrung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators, Int. J. Intell. Syst., 35 (2020), 217–249. https://doi.org/10.1002/int.22136 doi: 10.1002/int.22136
    [27] C. Que, Y. Wang, Y. Lan, Hesitant fuzzy TOPSIS method of attribute association based on cumulative prospect theory, Stat. Decis., 34 (2018), 43–48.
    [28] G. Loomes, R. Sugden, Regret theory: An alternative theory of rational choice under uncertainty, Econ. J., 92 (1982), 805–824. https://doi.org/10.2307/2232669 doi: 10.2307/2232669
    [29] J. Quiggin, Regret theory with general choice sets, J. Risk Uncertainty, 8 (1994), 153–165, 1994. https://doi.org/10.1007/BF01065370 doi: 10.1007/BF01065370
    [30] W. Liang, Y. Wang, Interval-valued hesitant fuzzy stochastic decision-making method based on regret theory, Int. J. Fuzzy Syst., 22 (2020), 1091–1103. https://doi.org/10.1007/s40815-020-00830-z doi: 10.1007/s40815-020-00830-z
    [31] L. Qian, S. Liu, Z. Fang, Grey risky multi-attribute decision-making method based on regret theory and EDAS, Grey Syst. Theory Appl., 9 (2019), 101–113.
    [32] X. Liu, J. Zhu, S. Zhang, S. Liu, Hesitant fuzzy stochastic multiple attribute decision making method based on regret theory and group satisfaction degree, Chin. J. Manage. Sci., 25 (2017), 171–178.
    [33] X. Chen, H. Li, C. Tan, An intuitionstic fuzzy factorial analysis model for multi-attribute decision-making under random environment, J. Oper. Res. Soc., 70 (2019), 81–100. https://doi.org/10.1080/01605682.2017.1421849 doi: 10.1080/01605682.2017.1421849
    [34] Y. Zheng, J. Xu, H. Chen, TOPSIS-based entropy measure for intuitionistic trapezoidal fuzzy sets and application to multi-attribute decision making, Math. Biosci. Eng., 17 (2020), 5604–5617. https://doi.org/10.3934/mbe.2020301 doi: 10.3934/mbe.2020301
    [35] H. Zhang, B. Qiu, M. Tang, M. He., Risk assessment model of agricultural products cold chain logistics based on the improved catastrophe progression method, J. Syst. Eng., 33 (2018), 412–421.
    [36] L. Wang, X. Chen, Y. Xu, M. Huang, A catastrophe progression approach based index sensitivity analysis model for the multivariate flooding process, Stochastic Environ. Res. Risk Assess., 32 (2018), 141–153. https://doi.org/10.1007/s00477-016-1339-y doi: 10.1007/s00477-016-1339-y
    [37] H. Liu, C. Ai, Empirical research on rural e-commerce development level index system based on catastrophe progression method, Cluster Comput., 22 (2019), 6101–6109. https://doi.org/10.1007/s10586-018-1829-4 doi: 10.1007/s10586-018-1829-4
    [38] J. Li, S. Liu, Green economy performance evaluation of iron and steel industry—A case study of baosteel, Soft Sci., 33 (2019), 94–98.
    [39] Y. Guo, Y. Jia, S. Bai, Project selection of science and technology park based on catastrophe progression method, Sci. Technol. Manage. Res., 37 (2017), 164–169.
    [40] X. Lv, X. Lu, and C. Wu, National ecological security evaluation based on VPRS and catastrophe progression, Syst. Eng., 36 (2018), 79–84.
    [41] H. Zhang, Y. Shi, B. Qiu, Applying catastrophe progression method to evaluate the service quality of cold chain logistics, Complex Intell. Syst., 1 (2020), 1–15. https://doi.org/10.1007/s40747-020-00202-y doi: 10.1007/s40747-020-00202-y
    [42] A. N. Gani, S. Assarudeen, A new operation on triangular fuzzy number for solving fuzzy linear programming problem, Appl. Math. Sci., 6 (2012), 525–532.
    [43] J. J. Huang, G. H. Tzeng, H. H. Liu, A revised VIKOR model for multiple criteria decision making -the perspective of regret theory, Commun. Comput. Inf. Sci., 35 (2009), 761–768. https://doi.org/10.1007/978-3-642-02298-2_112 doi: 10.1007/978-3-642-02298-2_112
    [44] N. Zhang, Y. Han, Q. Si, G. Wei, A novel method for multi-attribute risk decision-making based on regret theoryand hybird information, J. Intell. Fuzzy Syst., 39 (2020), 6955–6964. https://doi.org/10.3233/JIFS-200081 doi: 10.3233/JIFS-200081
    [45] A. Zhao, J. Guo, C. Wu, Evaluation on green growth of China: Based on integration with rough set, catastrophe progression model and topsis method, Technol. Econ., 36 (2017), 121–128.
    [46] A. Singh, A. Gupta, Best criteria selection based PROMETHEE Ⅱ to aid decision-making under 2-tuple linguistic framework: case-study of the most energy efficient region worldwide, Int. J. Manage. Decis. Making, 19 (2020), 44–65.
    [47] J. Zha, G. Song, Green building supplier selection based on catastrophe progression method, J. Eng. Manage., 29 (2015), 43–47.
    [48] H. Wang, X. Lu, Y. Du, C. Zhang, R. Sadiq, Y. Deng, Fault tree analysis based on TOPSIS and triangular fuzzy number, Int. J. Syst. Assur. Eng. Manage., 8 (2017), 2064–2070. https://doi.org/10.1007/s13198-014-0323-5 doi: 10.1007/s13198-014-0323-5
    [49] C. Dong, C. Zhang, B. Wang, Integration of green quality function deployment and fuzzy multi-attribute utility theory-based cost estimation for environmentally conscious product development, Int. J. Environ. Conscious Design Manuf., 11 (2003), 12–28.
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