A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 6
Jun.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
D. García-Zamora, Á. Labella, W. Ding, R. M. Rodríguez, and  L. Martínez,  “Large-scale group decision making: A systematic review and a critical analysis,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 949–966, Jun. 2022. doi: 10.1109/JAS.2022.105617
Citation: D. García-Zamora, Á. Labella, W. Ding, R. M. Rodríguez, and  L. Martínez,  “Large-scale group decision making: A systematic review and a critical analysis,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 949–966, Jun. 2022. doi: 10.1109/JAS.2022.105617

Large-Scale Group Decision Making: A Systematic Review and a Critical Analysis

doi: 10.1109/JAS.2022.105617
Funds:  This work was partially supported by the Spanish Ministry of Economy and Competitiveness through the Spanish National Project PGC2018-099402-B-I00, and the Postdoctoral fellow Ramón y Cajal (RYC-2017-21978), the FEDER-UJA project 1380637 and ERDF, the Spanish Ministry of Science, Innovation and Universities through a Formación de Profesorado Universitario (FPU2019/01203) grant, the Junta de Andalucía, Andalusian Plan for Research, Development, and Innovation (POSTDOC 21-00461), the National Natural Science Foundation of China (61300167, 61976120), the Natural Science Foundation of Jiangsu Province (BK20191445), the Natural Science Key Foundation of Jiangsu Education Department (21KJA510004), and Qing Lan Project of Jiangsu Province
More Information
  • The society in the digital transformation era demands new decision schemes such as e-democracy or based on social media. Such novel decision schemes require the participation of many experts/decision makers/stakeholders in the decision processes. As a result, large-scale group decision making (LSGDM) has attracted the attention of many researchers in the last decade and many studies have been conducted in order to face the challenges associated with the topic. Therefore, this paper aims at reviewing the most relevant studies about LSGDM, identifying the most profitable research trends and analyzing them from a critical point of view. To do so, the Web of Science database has been consulted by using different searches. From these results a total of 241 contributions were found and a selection process regarding language, type of contribution and actual relation with the studied topic was then carried out. The 87 contributions finally selected for this review have been analyzed from four points of view that have been highly remarked in the topic, such as the preference structure in which decision-makers’ opinions are modeled, the group decision rules used to define the decision making process, the techniques applied to verify the quality of these models and their applications to real world problems solving. Afterwards, a critical analysis of the main limitations of the existing proposals is developed. Finally, taking into account these limitations, new research lines for LSGDM are proposed and the main challenges are stressed out.

     

  • loading
  • [1]
    S. V. Kovalchuk, V. Krotov, A. Smirnov, D. A. Nasonov, and A. N. Yakovlev, “Distributed data-driven platform for urgent decision making in cardiological ambulance control,” Future Generation Computer Systems-the Int. Journal of Escience, vol. 79, pp. 144–154, 2018. doi: 10.1016/j.future.2016.09.017
    [2]
    Q. Q. Long, “Data-driven decision making for supply chain networks with agent-based computational experiment,” Knowledge-Based Systems, vol. 141, pp. 55–66, 2018. doi: 10.1016/j.knosys.2017.11.006
    [3]
    I. Palomares, R. M. Rodriguez, and L. Martinez, “An attitude-driven web consensus support system for heterogeneous group decision making,” Expert Systems with Applications, vol. 40, pp. 139–149, 2013. doi: 10.1016/j.eswa.2012.07.029
    [4]
    Y. Sun, S. He, and J. Leu, “Syndicating web services: A QoS and userdriven approach,” Decision Support Systems, vol. 43, 2007.
    [5]
    Eklund, A. Rusinowska, and H. de Swart, “Consensus reaching in committees,” Decision Support, vol. 178, no. 1, pp. 185–193, 2007.
    [6]
    Eklund, A. Rusinowska, and H. de Swart, “A consensus model of political decision-making,” Annals of Operations Research, vol. 158, no. 1, pp. 5–20, 2008. doi: 10.1007/s10479-007-0249-2
    [7]
    I. Palomares, L. Martinez, and F. Herrera, “A consensus model to detect and manage noncooperative behaviors in large-scale group decision making,” IEEE Trans. Fuzzy Systems, vol. 22, no. 3, pp. 516–530, 2014. doi: 10.1109/TFUZZ.2013.2262769
    [8]
    Z. Zhang, C. H. Guo, and L. Martinez, “Managing multigranular linguistic distribution assessments in large-scale multiattribute group decision making,” IEEE Trans. Systems Man Cybernetics Systems, vol. 47, no. 11, pp. 3063–3076, 2017. doi: 10.1109/TSMC.2016.2560521
    [9]
    J. Kim, “A model and case for supporting participatory public decision making in e-democracy,” Group Decision and Negotiation, vol. 17, no. 3, pp. 179–193, 2008. doi: 10.1007/s10726-007-9075-9
    [10]
    A. Mateos, A. Jiménez-Martín, and S. Ríos-Insua, “A group decisionmaking methodology with incomplete individual beliefs applied to e-democracy,” Group Decision and Negotiation, vol. 24, no. 4, pp. 633–653, 2015. doi: 10.1007/s10726-014-9401-y
    [11]
    R. Karthik and S. Ganapathy, “A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce,” Applied Soft Computing, vol. 108, p. 107396, 2021.
    [12]
    T. T. Tham and H. Nguyen, “An integrated approach of fuzzy-ahptopsis for e-commerce evaluation,” Industrial Engineering &Management Systems, vol. 20, no. 2, pp. 82–95, 2021.
    [13]
    C. Sueur, J. L. Deneubourg, and O. Petit, “From social network (centralized vs. decentralized) to collective decision-making (unshared vs. shared consensus),” PLoS one, vol. 7, no. 2, p. e32566, 2012.
    [14]
    Y. J. Xu, X. W. Wen, and W. C. Zhang, “A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection,” Computers &Industrial Engineering, vol. 116, pp. 113–129, 2018.
    [15]
    H. J. Zhang, J. Xiao, I. Palomares, H. M. Liang, and Y. C. Dong, “Linguistic distribution-based optimization approach for large-scale gdm with comparative linguistic information: An application on the selection of wastewater disinfection technology,” IEEE Trans. Fuzzy Systems, vol. 28, no. 2, pp. 376–389, 2020. doi: 10.1109/TFUZZ.2019.2906856
    [16]
    X. J. Gou, Z. S. Xu, and F. Herrera, “Consensus reaching process for large-scale group decision making with double hierarchy hesitant fuzzy linguistic preference relations,” Knowledge-Based Systems, vol. 157, pp. 20–33, 2018. doi: 10.1016/j.knosys.2018.05.008
    [17]
    R. X. Ding, I. Palomares, X. Q. Wang, G. R. Yang, B. S. Liu, Y. C. Dong, E. Herrera-Viedma, and F. Herrera, “Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective,” Information Fusion, vol. 59, pp. 84–102, 2020. doi: 10.1016/j.inffus.2020.01.006
    [18]
    R. M. Rodriguez, A. Labella, G. De Tre, and L. Martinez, “A large scale consensus reaching process managing group hesitation,” Knowledge-Based Systems, vol. 159, pp. 86–97, 2018.
    [19]
    R. X. Ding, X. Q. Wang, K. Shang, B. S. Liu, and F. Herrera, “Sparse representation-based intuitionistic fuzzy clustering approach to find the group intra-relations and group leaders for large-scale decision making,” IEEE Trans. Fuzzy Systems, vol. 27, no. 3, pp. 559–573, 2019. doi: 10.1109/TFUZZ.2018.2864661
    [20]
    A. Labella, Y. Liu, R. M. Rodriguez, and L. Martinez, “Analyzing the performance of classical consensus models in large scale group decision making: A comparative study,” Applied Soft Computing, vol. 67, pp. 677–690, 2018. doi: 10.1016/j.asoc.2017.05.045
    [21]
    R. X. Nie, Z. Tian, J. Q. Wang, and H. Y. Luo, “An objective and interactive-information-based feedback mechanism for the consensus-reaching process considering a non-support degree for minority opinions,” Expert Systems, vol. 37, no. 5, 2020.
    [22]
    F. Liu, J. W. Zhang, and T. Liu, “A PSO-algorithm-based consensus model with the application to large-scale group decision-making,” Complex &Intelligent Systems, vol. 6, no. 2, pp. 287–298, 2020.
    [23]
    K. Kabirifar and M. Mojtahedi, “The impact of engineering, procurement and construction (EPC) phases on project performance: A case of large-scale residential construction project,” Buildings, vol. 9, no. 1, 2019.
    [24]
    G. Carvalho, A. S. Vivacqua, J. M. Souza, and S. J. Medeiros, “LaSca: A large scale group decision support system,” Journal of Universal Computer Science, vol. 17, no. 2, pp. 261–275, 2011.
    [25]
    I. Palomares, L. Martinez, and F. Herrera, “Mentor: A graphical monitoring tool of preferences evolution in large-scale group decision making,” Knowledge-Based Systems, vol. 58, pp. 66–74, 2014. doi: 10.1016/j.knosys.2013.07.003
    [26]
    H. J. Zhang, S. H. Zhao, G. Kou, C. C. Li, Y. C. Dong, and F. Herrera, “An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges,” Information Fusion, vol. 60, pp. 65–79, 2020. doi: 10.1016/j.inffus.2020.03.001
    [27]
    M. Tang and H. Liao, “From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey” Omega, vol. 100, 2021.
    [28]
    J. Kacprzyk, “Group decision making with a fuzzy linguistic majority,” Fuzzy Sets and Systems, vol. 18, no. 2, pp. 105–118, 1986. doi: 10.1016/0165-0114(86)90014-X
    [29]
    J. Lu and D. Ruan, “Multi-objective group decision making: Methods, software and applications with fuzzy set techniques,” Imperial College Press, vol. 6, 2007.
    [30]
    M. Roubens, “Fuzzy sets and decision analysis,” Fuzzy Sets and Systems, vol. 90, no. 2, pp. 199–206, 1997.
    [31]
    C. Butler and A. Rothstein, On Conflict and Consensus: A Handbook on Formal Consensus Decision Making. Portland: Food Not Bombs Publishing, 1987.
    [32]
    I. Palomares, F. J. Estrella, L. Martínez, and F. Herrera, “Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study,” Information Fusion, vol. 20, pp. 252–271, 2014. doi: 10.1016/j.inffus.2014.03.002
    [33]
    E. Herrera-Viedma, J. L. García-Lapresta, J. Kacprzyk, M. Fedrizzi, H. Nurmi, and S. Zadrożny, Consensual Processes. Springer, 2011, vol. 267.
    [34]
    S. Modgil, S. Gupta, U. Sivarajah, and B. Bhushan, “Big data-enabled large-scale group decision making for circular economy: An emerging market context,” Technological Forecasting and Social Change, vol. 166, 2021.
    [35]
    T. Wu, X. W. Liu, and J. D. Qin, “A linguistic solution for double largescale group decision-making in e-commerce,” Computers &Industrial Engineering, vol. 116, pp. 97–112, 2018.
    [36]
    Q. F. Wan, X. H. Xu, X. H. Chen, and J. Zhuang, “A two-stage optimization model for large-scale group decision-making in disaster management: Minimizing group conflict and maximizing individual satisfaction,” Group Decision and Negotiation, vol. 29, no. 5, pp. 901–921, 2020. doi: 10.1007/s10726-020-09684-0
    [37]
    L. M. Liu, W. Z. Cao, B. Shi, and M. Tang, “Large-scale green supplier selection approach under a q-rung interval-valued orthopair fuzzy environment,” Processes, vol. 7, no. 9, 2019.
    [38]
    Y. C. Dong, S. H. Zhao, H. J. Zhang, F. Chiclana, and E. Herrera-Viedma, “A self-management mechanism for noncooperative behaviors in large-scale group consensus reaching processes,” IEEE Trans. Fuzzy Systems, vol. 26, no. 6, pp. 3276–3288, 2018. doi: 10.1109/TFUZZ.2018.2818078
    [39]
    Z. B. Wu and J. Xu, “A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters,” Information Fusion, vol. 41, pp. 217–231, 2018. doi: 10.1016/j.inffus.2017.09.011
    [40]
    B. S. Liu, Q. Zhou, R. X. Ding, I. Palomares, and F. Herrera, “Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination,” European Journal of Operational Research, vol. 275, no. 2, pp. 737–754, 2019. doi: 10.1016/j.ejor.2018.11.075
    [41]
    Y. Lu, Y. Xu, E. Herrera-Viedma, and Y. Han, “Consensus of large-scale group decision making in social network: The minimum cost model based on robust optimization,” Information Sciences, vol. 547, pp. 910–930, 2021. doi: 10.1016/j.ins.2020.08.022
    [42]
    Z. J. Shi, X. Q. Wang, I. Palomares, S. J. Guo, and R. X. Ding, “A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior classification and adaptive weight updating,” Knowledge-Based Systems, vol. 158, pp. 196–208, 2018. doi: 10.1016/j.knosys.2018.06.002
    [43]
    B. A. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” Keele University, Tech. Rep. EBSE 2007-001, 2007.
    [44]
    I. Palomares, “Consensus model for large-scale group decision support in IT services management,” Intelligent Decision Technologies Netherlands, vol. 8, no. 2, pp. 81–94, 2014. doi: 10.3233/IDT-130180
    [45]
    S. Orlovsky, “Decision-making with a fuzzy preference relation,” Fuzzy Sets and Systems, vol. 1, no. 3, pp. 155–167, 1978. doi: 10.1016/0165-0114(78)90001-5
    [46]
    T. L. Saaty, “The analytic hierarchy process (AHP),” The Journal of the Operational Research Society, vol. 41, no. 11, pp. 1073–1076, 1980.
    [47]
    F. Seo and M. Sakawa, “Fuzzy multiattribute utility analysis for collective choice,” IEEE Trans. Systems,Man,and Cybernetics, vol. 1, pp. 45–53, 1985.
    [48]
    T. Tanino, “On group decision making under fuzzy preferences,” in Multiperson Decision Making Models Using Fuzzy Sets and Possibility Theory. Springer, 1990, pp. 172–185.
    [49]
    J. Xiao, X. L. Wang, and H. J. Zhang, “Managing personalized individual semantics and consensus in linguistic distribution large-scale group decision making,” Information Fusion, vol. 53, pp. 20–34, 2020. doi: 10.1016/j.inffus.2019.06.003
    [50]
    C. C. Li, Y. C. Dong, and F. Herrera, “A consensus model for largescale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups,” IEEE Trans. Fuzzy Systems, vol. 27, no. 2, pp. 221–233, 2019. doi: 10.1109/TFUZZ.2018.2857720
    [51]
    T. Wu, X. W. Liu, and F. Liu, “An interval type-2 fuzzy topsis model for large scale group decision making problems with social network information,” Information Sciences, vol. 432, pp. 392–410, 2018. doi: 10.1016/j.ins.2017.12.006
    [52]
    W. J. Zuo, D. F. Li, G. F. Yu, and L. Zhang, “A large group decision-making method and its application to the evaluation of property perceived service quality,” Journal of Intelligent &Fuzzy Systems, vol. 37, no. 1, pp. 1513–1527, 2019.
    [53]
    Y. M. Song and J. Hu, “Large-scale group decision making with multiple stakeholders based on probabilistic linguistic preference relation,” Applied Soft Computing, vol. 80, pp. 712–722, 2019. doi: 10.1016/j.asoc.2019.04.036
    [54]
    Y. M. Song and G. X. Li, “Consensus constructing in large-scale group decision making with multi-granular probabilistic 2-tuple fuzzy linguistic preference relations,” IEEE Access, vol. 7, pp. 56 947–56 959, 2019. doi: 10.1109/ACCESS.2019.2913546
    [55]
    S. L. Li and C. Wei, “A two-stage dynamic influence model-achieving decision-making consensus within large scale groups operating with incomplete information,” Knowledge-Based Systems, vol. 189, 2020.
    [56]
    J. F. Chu, Y. M. Wang, X. W. Liu, and Y. C. Liu, “Social network community analysis based large-scale group decision making approach with incomplete fuzzy preference relations,” Information Fusion, vol. 60, pp. 98–120, 2020. doi: 10.1016/j.inffus.2020.02.005
    [57]
    Y. M. Song and G. X. Li, “A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection,” Journal of the Operational Research Society, vol. 70, no. 5, pp. 827–841, 2019. doi: 10.1080/01605682.2018.1458017
    [58]
    H. Liao, R. Tan, and M. Tang, “An overlap graph model for largescale group decision making with social trust information considering the multiple roles of experts,” Expert Systems, vol. 38, no. 3, 2021.
    [59]
    R. X. Ding, X. Q. Wang, K. Shang, and F. Herrera, “Social network analysis-based conflict relationship investigation and conflict degree-based consensus reaching process for large scale decision making using sparse representation,” Information Fusion, vol. 50, pp. 251–272, 2019. doi: 10.1016/j.inffus.2019.02.004
    [60]
    T. Wu, K. Zhang, X. W. Liu, and C. Y. Cao, “A two-stage social trust network partition model for large-scale group decision-making problems,” Knowledge-Based Systems, vol. 163, pp. 632–643, 2019. doi: 10.1016/j.knosys.2018.09.024
    [61]
    Z. J. Du, S. M. Yu, H. Y. Luo, and X. D. Lin, “Consensus convergence in large-group social network environment: Coordination between trust relationship and opinion similarity,” Knowledge-Based Systems, vol. 217, p. 106828, 2021.
    [62]
    M. Tang, H. Liao, E. Herrera-Viedma, C. L. Chen, and W. Pedrycz, “A dynamic adaptive subgroup-to-subgroup compatibility-based conflict detection and resolution model for multicriteria large-scale group decision making,” IEEE Trans. Cybernetics, vol. 51, no. 10, pp. 4784–4795, 2021.
    [63]
    Z. J. Du, S. M. Yu, and X. H. Xu, “Managing noncooperative behaviors in large-scale group decision-making: Integration of independent and supervised consensus-reaching models,” Information Sciences, vol. 531, pp. 119–138, 2020. doi: 10.1016/j.ins.2020.03.100
    [64]
    Z. J. Du, H. Y. Luo, X. D. Lin, and S. M. Yu, “A trust-similarity analysis-based clustering method for large-scale group decision-making under a social network,” Information Fusion, vol. 63, pp. 13–29, 2020. doi: 10.1016/j.inffus.2020.05.004
    [65]
    X. H. Xu, Z. J. Du, X. H. Chen, and C. G. Cai, “Confidence consensus-based model for large-scale group decision making: A novel approach to managing non-cooperative behaviors,” Information Sciences, vol. 477, pp. 410–427, 2019. doi: 10.1016/j.ins.2018.10.058
    [66]
    C. X. Zhang, M. Zhao, L. C. Zhao, and Q. F. Yuan, “A consensus model for large-scale group decision-making based on the trust relationship considering leadership behaviors and non-cooperative behaviors,” Group Decision and Negotiation, vol. 30, no. 3, pp. 553–586, 2021. doi: 10.1007/s10726-021-09723-4
    [67]
    T. Wu, X. W. Liu, J. D. Qin, and F. Herrera, “Balance dynamic clustering analysis and consensus reaching process with consensus evolution networks in large-scale group decision making,” IEEE Trans. Fuzzy Systems, vol. 29, no. 2, pp. 357–371, 2021. doi: 10.1109/TFUZZ.2019.2953602
    [68]
    Z. Y. Liu, X. He, and Y. Deng, “Network-based evidential three-way theoretic model for large-scale group decision analysis,” Information Sciences, vol. 547, pp. 689–709, 2021. doi: 10.1016/j.ins.2020.08.042
    [69]
    M. Tang, H. Liao, X. Mi, X. Xu, and F. Herrera, “Dynamic subgroup-quality-based consensus in managing consistency, nearness, and evenness quality indices for large-scale group decision making under hesitant environment,” Journal of the Operational Research Society, vol. 72, no. 4, pp. 865–878, 2020.
    [70]
    M. Tang, H. C. Liao, J. Xu, D. Streimikiene, and X. S. Zheng, “Adaptive consensus reaching process with hybrid strategies for large-scale group decision making,” European Journal of Operational Research, vol. 282, no. 3, pp. 957–971, 2020. doi: 10.1016/j.ejor.2019.10.006
    [71]
    F. J. Quesada, I. Palomares, and L. Martinez, “Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators,” Applied Soft Computing, vol. 35, pp. 873–887, 2015. doi: 10.1016/j.asoc.2015.02.040
    [72]
    Q. Gao, J. Huang, and Y. J. Xu, “A k-core decomposition-based opinion leaders identifying method and clustering-based consensus model for large-scale group decision making,” Computers &Industrial Engineering, vol. 150, 2020.
    [73]
    W u, Q. Wu, L. G. Zhou, and H. Y. Chen, “Optimal group selection model for large-scale group decision making,” Information Fusion, vol. 61, pp. 1–12, 2020. doi: 10.1016/j.inffus.2020.03.002
    [74]
    X. Liu, Y. J. Xu, R. Montes, R. X. Ding, and F. Herrera, “Alternative ranking-based clustering and reliability index-based consensus reaching process for hesitant fuzzy large scale group decision making,” IEEE Trans. Fuzzy Systems, vol. 27, no. 1, pp. 159–171, 2019. doi: 10.1109/TFUZZ.2018.2876655
    [75]
    D. L. Zhang, Y. B. Yang, W. C. Wang, and X. S. You, “A LSGDM method based on social network and IVIFN’s geometric characteristics for evaluating the collaborative innovation problem,” Journal of Intelligent &Fuzzy Systems, vol. 40, no. 3, pp. 5119–5138, 2021.
    [76]
    Y. W. Du, N. Yang, and J. Ning, “IFS/ER-based large-scale multiattribute group decision-making method by considering expert knowledge structure,” Knowledge-Based Systems, vol. 162, pp. 124–135, 2018. doi: 10.1016/j.knosys.2018.07.034
    [77]
    Y. W. Du and Y. K. Shan, “A dynamic intelligent recommendation method based on the analytical ER rule for evaluating product ideas in large-scale group decision-making,” Group Decision and Negotiation, 2020.
    [78]
    Y. W. Du, Q. Chen, Y. L. Sun, and C. H. Li, “Knowledge structurebased consensus-reaching method for large-scale multiattribute group decision-making,” Knowledge-Based Systems, vol. 219, 2021.
    [79]
    E. Koksalmis and O. Kabak, “Sensor fusion based on Dempster-Shafer theory of evidence using a large scale group decision making approach,” Int. Journal of Intelligent Systems, vol. 35, no. 7, pp. 1126–1162, 2020. doi: 10.1002/int.22237
    [80]
    S. F. He, X. H. Pan, and Y. M. Wang, “A shadowed set-based todim method and its application to large-scale group decision making,” Information Sciences, vol. 544, pp. 135–154, 2021. doi: 10.1016/j.ins.2020.07.028
    [81]
    M. Zhao, M. Gao, and Z. C. Li, “A consensus model for large-scale multi-attribute group decision making with collaboration-reference network under uncertain linguistic environment,” Journal of Intelligent &Fuzzy Systems, vol. 37, no. 3, pp. 4133–4156, 2019.
    [82]
    P. Wang, X. H. Xu, and S. Huang, “An improved consensus-based model for large group decision making problems considering experts with linguistic weighted information,” Group Decision and Negotiation, vol. 28, no. 3, pp. 619–640, 2019. doi: 10.1007/s10726-019-09615-8
    [83]
    B. S. Liu, T. F. Huo, C. Liao, J. Gong, and B. Xue, “A group decision-making aggregation model for contractor selection in large scale construction projects based on two-stage partial least squares (PLS) path modeling,” Group Decision and Negotiation, vol. 24, no. 5, pp. 855–883, 2015. doi: 10.1007/s10726-014-9418-2
    [84]
    X. Gou, Z. Xu, H. Liao, and F. Herrera, “Consensus model handling minority opinions and noncooperative behaviors in large-scale group decision-making under double hierarchy linguistic preference relations,” IEEE Trans. Cybernetics, vol. 51, no. 1, pp. 283–296, 2021. doi: 10.1109/TCYB.2020.2985069
    [85]
    Q. Zhan, C. Fu, and M. Xue, “Distance-based large-scale group decision-making method with group influence,” Int. Journal of Fuzzy Systems, vol. 23, no. 2, pp. 535–554, 2021. doi: 10.1007/s40815-020-00993-9
    [86]
    R. X. Ren, M. Tang, and H. C. Liao, “Managing minority opinions in micro-grid planning by a social network analysis-based large scale group decision making method with hesitant fuzzy linguistic information,” Knowledge-Based Systems, vol. 189, 2020.
    [87]
    X. Y. Zhong and X. H. Xu, “Clustering-based method for large group decision making with hesitant fuzzy linguistic information: Integrating correlation and consensus,” Applied Soft Computing, vol. 87, 2020.
    [88]
    Z. Zhang, W. Y. Yu, L. Martinez, and Y. Gao, “Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute largescale group decision making: A linguistic distribution-based approach,” IEEE Trans. Fuzzy Systems, vol. 28, no. 11, pp. 2875–2889, 2020. doi: 10.1109/TFUZZ.2019.2949758
    [89]
    L. Xiao, Z. S. Chen, X. Zhang, J. Chang, W. Pedrycz, and K. S. Chin, “Bid evaluation for major construction projects under large-scale group decision-making environment and characterized expertise levels,” Int. Journal of Computational Intelligence Systems, vol. 13, no. 1, pp. 1227–1242, 2020. doi: 10.2991/ijcis.d.200801.002
    [90]
    S. L. Li and C. Wei, “A large scale group decision making approach in healthcare service based on sub-group weighting model and hesitant fuzzy linguistic information,” Computers &Industrial Engineering, vol. 144, 2020.
    [91]
    Z. Z. Ma, J. J. Zhu, K. Ponnambalam, and S. T. Zhang, “A clustering method for large-scale group decision-making with multi-stage hesitant fuzzy linguistic terms,” Information Fusion, vol. 50, pp. 231–250, 2019. doi: 10.1016/j.inffus.2019.02.001
    [92]
    X. L. Wu, S. Nie, H. C. Liao, and Gupta, “A large-scale group decision making method with a consensus reaching process under cognitive linguistic environment,” Int. Transactions in Operational Research, 2020.
    [93]
    Z. Tian, R. X. Nie, and J. Q. Wang, “Social network analysis-based consensus-supporting framework for large-scale group decision-making with incomplete interval type-2 fuzzy information,” Information Sciences, vol. 502, pp. 446–471, 2019. doi: 10.1016/j.ins.2019.06.053
    [94]
    T. Wu, X. W. Liu, and F. Liu, “The solution for fuzzy large-scale group decision making problems combining internal preference information and external social network structures,” Soft Computing, vol. 23, no. 18, pp. 9025–9043, 2019. doi: 10.1007/s00500-018-3512-3
    [95]
    T. Wu and X. W. Liu, “An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems,” Knowledge-Based Systems, vol. 114, pp. 118–127, 2016. doi: 10.1016/j.knosys.2016.10.004
    [96]
    Z. Wen and H. C. Liao, “Capturing attitudinal characteristics of decision-makers in group decision making: Application to select policy recommendations to enhance supply chain resilience under COVID-19 outbreak,” Operations Management Research, 2021.
    [97]
    X. Tan, J. J. Zhu, F. J. Cabrerizo, and E. Herrera-Viedma, “A cyclic dynamic trust-based consensus model for large-scale group decision making with probabilistic linguistic information,” Applied Soft Computing, vol. 100, 2021.
    [98]
    Q. Liu, H. Y. Wu, and Z. S. Xu, “Consensus model based on probability k-means clustering algorithm for large scale group decision making,” Int. Journal of Machine Learning and Cybernetics, vol. 12, no. 6, pp. 1609–1626, 2021. doi: 10.1007/s13042-020-01258-5
    [99]
    B. L. Wang and J. Y. Liang, “A novel preference measure for multigranularity probabilistic linguistic term sets and its applications in large-scale group decision-making,” Int. Journal of Fuzzy Systems, vol. 22, no. 7, pp. 2350–2368, 2020. doi: 10.1007/s40815-020-00887-w
    [100]
    X. Li, H. Liao, and Z. Wen, “A consensus model to manage the noncooperative behaviors of individuals in uncertain group decision making problems during the COVID-19 outbreak,” Applied Soft Computing, vol. 99, p. 106879, 2021.
    [101]
    X. R. Chao, G. Kou, Y. Peng, and E. Herrera-Viedma, “Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion,” European Journal of Operational Research, vol. 288, no. 1, pp. 271–293, 2021. doi: 10.1016/j.ejor.2020.05.047
    [102]
    M. Tang, X. Y. Zhou, H. C. Liao, J. Xu, H. Fujita, and F. Herrera, “Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making,” Knowledge-Based Systems, vol. 180, pp. 62–74, 2019. doi: 10.1016/j.knosys.2019.05.019
    [103]
    X. Liu, Y. J. Xu, and F. Herrera, “Consensus model for largescale group decision making based on fuzzy preference relation with self-confidence: Detecting and managing overconfidence behaviors,” Information Fusion, vol. 52, pp. 245–256, 2019. doi: 10.1016/j.inffus.2019.03.001
    [104]
    Y. Cornet, M. J. Barradale, H. Gudmundsson, and M. B. Barfod, “Engaging multiple actors in large-scale transport infrastructure project appraisal: An application of MAMCA to the case of HS2 high-speed rail,” Journal of Advanced Transportation, 2018.
    [105]
    G. Beliakov, H. Bustince, and T. Calvo, A Practical Guide to Averaging Functions. Springer, 01 2016.
    [106]
    X. Y. Mo, Z. S. Xu, H. Zhao, Z. N. Hao, and S. K. Xiang, “Hesitant fuzzy multiple integrals for information aggregation,” Int. Journal of Fuzzy Systems, vol. 22, no. 2, pp. 668–685, 2020. doi: 10.1007/s40815-019-00748-1
    [107]
    E. Kian, M. Sun, and F. Bosche, “Complexity for megaprojects in the energy sector,” Journal of Management in Engineering, vol. 33, Article No. 4, 2017.
    [108]
    X. K. Zhou, W. Liang, S. Z. Huang, and M. Fu, “Social recommendation with large-scale group decision-making for cyber-enabled online service,” IEEE Trans. Computational Social Systems, vol. 6, no. 5, pp. 1073–1082, 2019. doi: 10.1109/TCSS.2019.2932288
    [109]
    R. Urena, F. Chiclana, and E. Herrera-Viedma, “DeciTrustNET: A graph based trust and reputation framework for social networks,” Information Fusion, vol. 61, pp. 101–112, 2020. doi: 10.1016/j.inffus.2020.03.006
    [110]
    L. Martínez and J. Montero, “Challenges for improving consensus reaching process in collective decisions,” New Mathematics and Natural Computation, vol. 3, no. 02, pp. 203–217, 2007. doi: 10.1142/S1793005707000720
    [111]
    Á. Labella, H. Liu, R. M. Rodríguez, and L. Martinez, “A cost consensus metric for consensus reaching processes based on a comprehensive minimum cost model,” European Journal of Operational Research, vol. 281, no. 2, pp. 316–331, 2020. doi: 10.1016/j.ejor.2019.08.030
    [112]
    D. García-Zamora, Á. Labella, R. M. Rodríguez, and L. Martínez, “Nonlinear preferences in group decision-making. Extreme values amplifications and extreme values reductions,” Int. Journal of Intelligent Systems, vol. 36, no. 11, pp. 6581–6612, 2021. doi: 10.1002/int.22561

Catalog

    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

    Figures(11)  / Tables(7)

    Article Metrics

    Article views (315) PDF downloads(131) Cited by()

    Highlights

    • Analysis of the current state of art about the existing trends related to the Large-scale Group Decision Making
    • Critical discussion about the main limitations of present proposals
    • Redirection of current research towards new trends which face real-world needs demanded by large-scale contexts

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return