A novel multiple attribute decision making method based on q-rung dual hesitant uncertain linguistic sets and Muirhead mean

• Autorzy: Wang Jun , Shang Xiaopu , Feng Xue , Sun Mengyang

Identyfikatory

DOI

10.24425/acs.2020.133499

• Języki publikacji: EN

Abstrakty

EN

This paper aims to propose a new multi-attribute decision making (MADM) method in complicated and fuzzy decision-making environment. To express both decision makers (DMs’) quantitative and qualitative evaluation information comprehensively and consider their high hesitancy in giving their assessment values in MADM process, we combine q-rung dual hesitant fuzzy sets (q-RDHFSs) with uncertain linguistic variables and develop a new tool, called the q-rung dual hesitant uncertain linguistic sets (q-RDHULSs). First, the definition, operations and comparison method of q-RDHULSs are proposed. Second, given the interrelationship among multiple q-rung dual hesitant uncertain linguistic variables (q-RDHULVs) we introduce some aggregation operators (AOs) to fuse q-rung dual hesitant uncertain linguistic (q-RDHUL) information based on the Muirhead mean, i.e. the q-RDHUL Muirhead mean operator, the q-RDHUL weighted Muirhead mean operator, the q-RDHUL dual Muirhead mean operator, and the q-RDHUL weighted dual Muirhead mean operator. To cope with MADM problems with q-RDHUL information, we propose a new method based on the proposed AOs. Afterwards, we apply the proposed method to an enterprise informatization level evaluation problem to verify its effectiveness. In addition, we also explain why our proposed method is more powerful and flexible than others.

Słowa kluczowe

EN

q-rung dual hesitant uncertain linguistic sets; Muirhead mean; q-rung dual hesitant uncertain linguistic Muirhead mean; multi-attribute decision making; informatization level evaluation

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2020
• Tom: Vol. 30, No. 2
• Strony: 233--272
• Opis fizyczny: Bibliogr. 57 poz., rys., tab., wzory

Twórcy

autor

Wang Jun

• School of School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

autor

Shang Xiaopu

• School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

autor

Feng Xue

• School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China

autor

Sun Mengyang

• China Institute of Marine Technology and Economy, Beijing 10081, China

Bibliografia

• [1] L. A. Zadeh: Fuzzy sets, Information Control, 8 (1965), 338-356.
• [2] W. S. Liu and H. C. Liao: A bibliometric analysis of fuzzy decision research during 1970-2015, International Journal of Fuzzy Systems, 19(1) (2017), 1-14.
• [3] V. Torra: Hesitant fuzzy sets, International Journal of Intelligent Systems, 25(6) (2010), 529-539.
• [4] X. Y. Tang and G. W. Wei: Dual hesitant Pythagorean fuzzy Bonferroni mean operators in multi-attribute decision making, Archives of Control Sciences, 29(2) (2019), 339–386.
• [5] H. B. Liu, Y. Ma, and L. Jiang: Managing incomplete preferences and consistency improvement in hesitant fuzzy linguistic preference relations with applications in group decision making, Information Fusion, 51 (2019), 19-29.
• [6] G. W. Wei and M. Lu: Dual hesitant Pythagorean fuzzy Hamacher aggregation operators in multiple attribute decision making, Archives of Control Sciences, 7(3) (2017), 365-395.
• [7] A. Ali and T. Rashid: Hesitant fuzzy best-worst multi-criteria decision-making method and its applications, International Journal of Intelligent Systems, 34(8) (2019), 1953-1967.
• [8] C. Y. Song, Y. X. Zhang, Z. S. Xu, Z. N. Hao, and X. X. Wang: Route selection of the Arctic Northwest Passage based on hesitant fuzzy decision field theory, IEEE Access, 7 (2019), 19919-19989.
• [9] X. W. Qi, J. L. Zhang, and C. Y. Liang: Multiple attributes group decision-making approaches based on interval-valued dual hesitant fuzzy unbalanced linguistic set and their applications, Complexity, 2018 (2018), Article ID 3172716, 22 pages.
• [10] H. Sellak, B. Ouhbi, B. Frikh, and B. Ikken: Expertise-based consensus building for MCGDM with hesitant fuzzy linguistic information, Information Fusion, 50 (2019), 54-70.
• [11] J. Li, J. Q. Wang, and J. H. Hu: Consensus building for hesitant fuzzy preference relations with multiplicative consistency, Computers & Industrial Engineering, 128 (2019), 387-400.
• [12] Y. M. Song and G. X Li: Handling group decision-making model with incomplete hesitant fuzzy preference relations and its application in medical decision, Soft Computing, 23(15) (2018), 6657-6666.
• [13] B. Zhu, Z. X. Xu, and M. M Xia: Dual hesitant fuzzy sets, Journal of Applied Mathematics, 2012 (2012), Article ID 879629, 13 pages.
• [14] H. J. Wang, X. F. Zhao, and G. W. Wei: Dual hesitant fuzzy aggregation operators in multiple attribute decision making, Journal of Intelligent & Fuzzy Systems, 26(5) (2014), 2281-2290.
• [15] D. J. Yu and D. F. Li: Dual hesitant fuzzy multi-criteria decision making and its application to teaching quality assessment, Journal of Intelligent & Fuzzy Systems, 27(4) (2014), 679-1688.
• [16] Y. P. Xing, R. T. Zhang, M. M. Xia, and J. Wang: Generalized point aggregation operators for dual hesitant fuzzy information, Journal of Intelligent & Fuzzy Systems, 33(1) (2017), 515-527.
• [17] A. Biswas and A. Sarkar: Some dual hesitant fuzzy Hamacher aggregation operators and their applications to multiple attribute decision making, Archives of Control Sciences, 27(5) (2018), 527–549.
• [18] L. Wang, Q. G. Shen, and L. Zhu: Dual hesitant fuzzy power aggregation operators based on Archimedean t-conorm and t-norm and their application to multiple attribute group decision making, Applied Soft Computing, 38 (2016), 23-50.
• [19] Y. B. Ju, X. Y. Liu, and S. H. Yang: Interval-valued dual hesitant fuzzy aggregation operators and their applications to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems, 27(3) (2014), 1203–1218.
• [20] X. D. Peng, J. G Dai, and L. Liu: Interval-valued dual hesitant fuzzy information aggregation and its application in multiple attribute decision making, International Journal for Uncertainty Quantification, 8(4) (2018), 361–382.
• [21] H. Zhao, Z. S. Xu, and S. S. Liu: Dual hesitant fuzzy information aggregation with Einstein t-conorm and t-norm, Journal of Systems Science and Systems Engineering, 26(2) (2017), 240-264.
• [22] Y. B. Ju, S. H. Yang, and X. Y. Liu: Some new dual hesitant fuzzy aggregation operators based on Choquet integral and their applications to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems, 27(6) (2014), 2857-2868.
• [23] Z. L. Ren, Z. S. Xu, and H. Wang: Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method, Information Sciences, 388 (2017), 1-16.
• [24] S. H. Yang and Y. B. Ju: Dual hesitant fuzzy linguistic aggregation operators and their applications to multi-attribute decision making, Journal of Intelligent & Fuzzy Systems, 27(4) (2014), 1935-1947.
• [25] Z. Su, Z. S. Xu, H. F. Liu, and S.S. Liu: Distance and similarity measures for dual hesitant fuzzy sets and their applications in pattern recognition, Journal of Intelligent & Fuzzy Systems, 29(2) (2015), 731-745.
• [26] P. D. Liu and G. L. Tang: Some generalized Shapely interval-valued dual hesitant fuzzy uncertain linguistic Choquet geometric operators and their application to multiple attribute decision making, Journal of Intelligent & Fuzzy Systems, 36(1) (2019), 557-574.
• [27] H. N. Tu, C. Y. Wang, X. Q. Zhou, and S. D. Tao: Dual hesitant fuzzy aggregation operators based on Bonferroni means and their applications to multiple attribute decision making, Annals of Fuzzy Mathematics and Informatics, 14(3) (2017), 265-278.
• [28] D. J. Yu, D. F. Li, and J. M. Merigo: Dual hesitant fuzzy group decision making method and its application to supplier selection, International Journal of Machine Learning and Cybernetics, 7(5) (2015), 819-831.
• [29] X. A. Tang, S. L. Yang, and W. Pedrycz: Multiple attribute decision-making approach based on dual hesitant fuzzy Frank aggregation operators, Applied Soft Computing, 68 (2018), 525-547.
• [30] H. D. Zhang, L. Shu, S. L. Liao, and C. R. Xiawu: Dual hesitant fuzzy rough set and its application, Soft Computing, 21(12) (2017), 3287-3305.
• [31] B. Y. Li, J. M. Xiao, and X. H. Wang: Interval-valued dual hesitant fuzzy rough set over two universes and its application, Journal of Intelligent & Fuzzy Systems, 35(3) (2018), 3195-3211.
• [32] D. C. Liang, Z. S. Xu, and D. Liu: Three-way decisions based on decision-theoretic rough sets with dual hesitant fuzzy information, Information Sciences, 396 (2017), 127-143.
• [33] H. D. Zhang and Y. P. He: A rough set-based method for dual hesitant fuzzy soft sets based on decision making, Journal of Intelligent & Fuzzy Systems, 35(3) (2018), 3437-3450.
• [34] H. Garg and R. Arora: Dual hesitant fuzzy soft aggregation operators and their application in decision-making, Cognitive Computation, 10(5) (2018), 769-789.
• [35] R. Arora and H. Garg: A robust correlation coefficient measure of dual hesitant fuzzy soft sets and their application in decision making, Engineering Applications of Artificial Intelligence, 72 (2018), 80-92.
• [36] B. Farhadinia: Correlation for dual hesitant fuzzy sets and dual interval-valued hesitant fuzzy sets, International Journal of Intelligent Systems, 29(2) (2014), 184-205.
• [37] S. K. Tyagi: Correlation coefficient of dual hesitant fuzzy sets and its applications, Applied Mathematical Modelling, 39(22) (2015), 7082-7092.
• [38] Y. Xu, X. P. Shang, J. Wang, W. Wu, and H. Q. Huang: Some q-rung dual hesitant fuzzy Heronian mean operators with their application to multiple attribute group decision-making, Symmetry, 10 (2018), 472.
• [39] P. D. Liu and J.L. Liu: Some q-rung orthopair fuzzy Bonferroni mean operators and their application to multi-attribute group decision making, International Journal of Intelligent Systems, 33(2) (2018), 315-347.
• [40] L. Li, R. T. Zhang, J. Wang, and X. P. Shang: Some q-rung orthopair linguistic Heronian mean operators with their application to multi-attribute group decision making, Archives of Control Sciences, 28(4) (2018), 551-583.
• [41] Z. M. Liu, S. Wang, and P. D. Liu: Multiple attribute group decision making based on q-rung orthopair fuzzy Heronian mean operators, International Journal of Intelligent Systems, 33(12) (2018), 2341-2363.
• [42] K. Y. Bai, X. M. Zhu, J. Wang, and R. T. Zhang: Some partitioned Maclaurin symmetric mean based on q-rung orthopair fuzzy information for dealing with multi-attribute group decision making, Symmetry, 10 (2018), 383.
• [43] L. Li, R.T. Zhang, J. Wang, X. Shang, and K.Y. Bai: A novel approach to multi-attribute group decision-making with q-rung picture linguistic information, Symmetry, 10 (2018), 172.
• [44] Y. P. Xing, R. T. Zhang, Z. Zhou, and J. Wang: Some q-rung orthopair fuzzy point weighted aggregation operators for multi-attribute decision making, Soft Computing, (2019), 1-23.
• [45] H. F. Cao, R. T. Zhang, and J. Wang: Some spherical linguistic Muirhead mean operators with their application to multi-attribute decision making, Journal of Intelligent & Fuzzy Systems, in press. doi: 10.3233/JIFS-190566.
• [46] Z. M. Liu, P. D. Liu, W. L. Liu, and J. Y. Pang: Pythagorean uncertain linguistic partitioned Bonferroni mean operators and their application in multi-attribute decision making, Journal of Intelligent & Fuzzy Systems, 32(3) (2017), 2779-2790.
• [47] Y. Q. Du, F. J. Hou, W. Zafar, Q. Yu, and Y. B. Zhai: A novel method for multiattribute decision making with interval-valued Pythagorean fuzzy linguistic information, International Journal of Intelligent Systems, 32(10) (2017), 1085-1112.
• [48] R. F. Muirhead: Some methods applicable to identities and inequalities of symmetric algebraic functions of n letters, Proceedings of the Edinburgh Mathematical Society 21 (1902), 144-162.
• [49] L. Li, R. T. Zhang, J. Wang, X. M. Zhu, and Y.P. Xing: Pythagorean fuzzy power Muirhead mean operators with their application to multi-attribute decision making, Journal of Intelligent & Fuzzy Systems, 35(2) (2018), 2035-2050.
• [50] P. D. Liu and X. L. You: Interval neutrosophic Muirhead mean operators and their application in multiple attribute group decision-making, International Journal for Uncertainty Quantification, 7(4) (2017), 303-334.
• [51] H. Garg: Multi-criteria decision-making method based on prioritized Muirhead mean aggregation operator under neutrosophic set environment, Symmetry, 10(7) (2018), 280.
• [52] Y. Xu, X. P. Shang, J. Wang, R. T. Zhang, W. Z. Li, and Y. P. Xing: A method to multi-attribute decision making with picture fuzzy information based on Muirhead mean, Journal of Intelligent & Fuzzy Systems, 36(4) (2018), 3833-3849.
• [53] P. D. Liu and D. F. Li: Some Muirhead mean operators for intuitionistic fuzzy numbers and their applications to group decision making, Plos One, 12(1) (2017), Article ID e0168767.
• [54] Z. S. Xu: Induced uncertain linguistic OWA operators applied to group decision making, Information Fusion, 7(2) (2006), 231-238.
• [55] P. D. Liu and F. Jin: Methods for aggregating intuitionistic uncertain linguistic variables and their application to group decision making, Information Sciences, 205 (2012), 58-71.
• [56] J. Wang, R. T. Zhang, L. Li, X.M. Zhu, and X. P. Shang: A novel approach to multi-attribute group decision making based on q-rung orthopair uncertain linguistic information, Journal of Intelligent & Fuzzy Systems, 36(6) (2019), 5565-5581.
• [57] P. D. Liu, Y. B. Chen, and Y. C. Chu: Intuitionistic uncertain linguistic weighted Bonferroni OWA operator and its application to multiple attribute decision making, Cybernetics and Systems, 45(5) (2014), 418-438.

Uwagi

EN

1. This work was supported by National Natural Science Foundation of China (61702023), Humanities and Social Science Foundation of Ministry of Education of China (17YJC870015), the Beijing Natural Science Foundation (7192107), and Funds for First-class Discipline Construction (XK1802-5).

PL

2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).