Abstract
Due to their rather practical nature, existing works on real-world implementations of Large Group Decision are summarized in this chapter, along with a brief overview of the real-world practical scenarios where many of the surveyed studies have been applied.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bolloju, N.: Aggregation of analytic hierarchy process models based on similarities in decision makers’ preferences. European Journal of Operational Research, 128, pp. 499–508, 2001.
Cai, C.-G., Xu, X.-H., Wang, P., Chen, X.-H.: A multi-stage conflict style large group emergency decision-making method. Soft Computing, 21, pp. 5765–5778, 2017.
Carvalho, G., Vivacqua, A.S., Souza, J.M., Medeiros, S.P.J.: LaSca: a Large Scale Group Decision Support System. Proceedings of 12th International Conference on Computer Supported Cooperative Work in Design. Xi’an (China), 2008.
Chiclana, F., Herrera-Viedma, E., Alonso, S., Marques-Pereira, R.: Preferences and consistency issues in group decision making. In H. Bustince et al. (Eds.): Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Intelligent Systems from Decision Making to Data Mining, Web Intelligence and Computer Vision. Studies in Fuzziness and Soft Computing, 220, pp. 219–237, Springer Verlag, 2008.
Chin, K.S., Xu, D.L., Yang, J.B., Lam, J.P.-K.: Group-based ER-AHP system for product project screening. Expert Systems with Applications, 35(4), pp. 1909–1929, 2008.
Cole, J.D., Sage, A.P.: Multi-person decision analysis in large scale systems - group decision making. Journal of the Franklin Institute, Apr 1975, pp. 245–268.
Crosscombe, M., Lawry, J.: Exploiting vagueness for multi-agent consensus. Multi-agent and Complex Systems, Studies in Computational Intelligence, vol. 670, pp. 67–78, Springer, 2017.
Dong, Y., Zhang, H., Herrera-Viedma, E.: Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decision Support Systems, 84, pp. 1–15, 2016.
Dong, Y., Ding, Z., Martinez, L., Herrera, F.: Managing consensus based on leadership in opinion dynamics. Information Sciences, 397–298, pp. 187–205, 2016.
Felfernig, A., Boratto, L., Stettinger, M., Tkalcic, M.: Group Recommender Systems - an Introduction. SpringerBriefs in Electrical and Computer Engineering, Springer, 2018.
Goel, A., Lee, D.T.: Towards large-scale deliberative decision-making: small groups and the importance of triads. EC ’16 Proceedings of the 2016 ACM Conference on Economics and Computation, pp. 287–303, 2016.
Husain, A.J.A.: A multi-agent system for scalable group decision making.
Kohonen, T.: Self-organizing maps. Heidelberg, Springer, 1995.
Li, Y., Lian, X., Lu, C., Wang, Z.: A large group decision making approach based on TOPSIS framework with unknown weights information. MATEC Web of Conferences (13th Global Congress on Manufacturing and Management, GCMM 2016), vol. 100, 2017.
Liu, H.C., You, X.Y., Tsung, F., Ji, P.: An improved approach for failure mode and effect analysis involving large group of experts: an application to the healthcare field. Quality Engineering, In press. https://doi.org/10.1080/08982112.2018.1448089.
Liu, B., Shen, Y., Chen, X., Sun, H., Chen, Y.: A complex multi-attribute large-group PLS decision-making method in the interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling, 38, pp. 4512–4527, 2014.
Liu, B., Huo, T., Liao, P., Gong, J., Xue, B.: 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, 24(5), pp. 855–883, 2014.
Liu, B., Shen, Y., Chen, Y., Chen, X., Wang, Y.: A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment. Information Fusion, 23, pp. 156–165, 2015.
Liu, Y., Fan, Z.P., Zhang, X.: A method for large group decision-making based on evaluation information provided by participators from multiple groups. Information Fusion, 29, pp. 132–141, 2016.
Muller, M.J.: Participatory Design: The Third Space in HCI. IBM Technical Report #01-04, 2002.
Xiang, L.: Energy network dispatch optimization under emergency of local energy shortage with web tool for automatic large group decision-making. Energy, 120, pp. 740–750, 2017.
Palomares, I., Quesada, F., Martinez, L.: Multi-agent-based semi-supervised consensus support system for large-scale group decision making. In Z. Wen and T. Li (eds.): Foundations of Intelligent Systems (ISKE 2013 Proceedings), Advances in Intelligent Systems and Computing 277, pp. 241–251, Springer, 2014.
Palomares, I., Martinez, L.: A semisupervised multiagent system model to support consensus-reaching processes. IEEE Transactions on Fuzzy Systems, 22(4), pp. 762–777, 2014.
Palomares, I., Martínez, L., Herrera, F.: A consensus model to detect and manage non-cooperative behaviors in large-scale group decision making. IEEE Transactions on Fuzzy Systems, 22(3), pp. 516–530, 2014.
Palomares, I., Martínez, L., Herrera, F.: MENTOR: A graphical monitoring tool of preferences evolution in large-scale group decision making. Knowledge-based Systems, 58 (Spec.Iss.), pp. 66–74, 2014.
Quesada, F., Palomares, I., Martínez, L.: Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators. Applied Soft Computing, 35, pp. 873–887, 2015.
Rodríguez, M.A.: Advances towards a general-purpose societal-scale human-collective problem-solving engine. Procs. 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 206–211, 2004.
Srdjevic, B.: Linking analytic hierarchy process and social choice methods to support group decision-making in water management. Decision Support Systems, 42, pp. 2261–2273, 2007.
Tapia-Rosero, A., De Tré, G.: Evaluating relevant opinions within a large group. Procs. International Conference on Fuzzy Computation Theory and Applications (FCTA-2014), pp. 76–86, 2014.
Turoff, M., Hiltz, S.R., Cho, H.-K., Li, Z., Wang, Y.: Social Decision Support Systems (SDSS). Procs. 35th Hawaii International Conference on System Sciences, 2002.
Wu, J., Chiclana, F., Herrera-Viedma, E.: Trust based consensus model for social network in an incomplete linguistic information context. Applied Soft Computing, 35, pp. 827–839, 2015.
Wu, T., Liu, X.W.: An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems. Knowledge-based Systems, 144, pp. 118–127, 2016.
Wu, T., Liu, X., Liu, F.: An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information. Information Sciences, 42, pp. 392–410, 2018.
Wu, Z., Xu, J.: A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Information Fusion, 41, pp. 217–231, 2018.
Xu, X.H., Liang, D., Chen, X., Zhou, Y.: A risk elimination coordination method for large group decision-making in natural disaster emergencies. Human and Ecological Risk Assessments: An International Journal, 21(5), pp. 1314–1325, 2014.
Xu, X.H., Du, Z.J., Chen, X.H.: Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions. Decision Support Systems, 79, pp. 150–160, 2015.
Xu, Y., Wen, X., Zhang, W.: A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Computers & Industrial Engineering, 116, pp. 113–129, 2018.
Yang, Y., Fu, C., Chen, Y.-W., Xu, D.-L., Yang, S.-L.: A belief rule based expert system for predicting consumer preference in new product development. Knowledge-based Systems, 94, pp. 105–113, 2016.
Yu, W., Zhang, Z., Zhong, Q.Y.: A TODIM-Based Approach to Large-Scale Group Decision Making with Multi-Granular Unbalanced Linguistic Information. Procs. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017.
Zahir, S.: Clusters in a group: decision making in the vector space formulation of the analytics hierarchy process. European Journal of Operational Research, 112, pp. 620–634, 1999.
Zhang, F., Ignatius, J., Lim, C.P., Goh, M.: A two-stage dynamic group decision making method for processing ordinal information. Knowledge-based Systems, 70, pp. 189–202, 2014.
Zhu, W.D., Liu, F., Chen, Y.W., Yang, J.B., Xu, D.L., Wang, D.P.: Research project evaluation and selection: an evidential reasoning rule-based method for aggregating peer review information with reliabilities. Scientometrics, 105(3), pp. 1469–1490, 2015.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 The Author(s), under exclusive licence to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Palomares Carrascosa, I. (2018). Implementations and Real-World Applications of LGDM Research. In: Large Group Decision Making. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-01027-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-01027-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01026-3
Online ISBN: 978-3-030-01027-0
eBook Packages: Computer ScienceComputer Science (R0)