An improved consensus-based group decision making model with heterogeneous information

Zhang, Feng, Ignatius, Joshua, Zhao, Yajun, Lim, Chee Peng, Ghasemi, Mohammadreza and Ng, Peh Sang 2015, An improved consensus-based group decision making model with heterogeneous information, Applied soft computing, vol. 35, pp. 850-863, doi: 10.1016/j.asoc.2015.03.055.

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Title An improved consensus-based group decision making model with heterogeneous information
Author(s) Zhang, Feng
Ignatius, Joshua
Zhao, Yajun
Lim, Chee PengORCID iD for Lim, Chee Peng
Ghasemi, Mohammadreza
Ng, Peh Sang
Journal name Applied soft computing
Volume number 35
Start page 850
End page 863
Total pages 14
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-10
ISSN 1568-4946
Summary In group decision making (GDM) problems, it is natural for decision makers (DMs) to provide different preferences and evaluations owing to varying domain knowledge and cultural values. When the number of DMs is large, a higher degree of heterogeneity is expected, and it is difficult to translate heterogeneous information into one unified preference without loss of context. In this aspect, the current GDM models face two main challenges, i.e., handling the complexity pertaining to the unification of heterogeneous information from a large number of DMs, and providing optimal solutions based on unification methods. This paper presents a new consensus-based GDM model to manage heterogeneous information. In the new GDM model, an aggregation of individual priority (AIP)-based aggregation mechanism, which is able to employ flexible methods for deriving each DM's individual priority and to avoid information loss caused by unifying heterogeneous information, is utilized to aggregate the individual preferences. To reach a consensus more efficiently, different revision schemes are employed to reward/penalize the cooperative/non-cooperative DMs, respectively. The temporary collective opinion used to guide the revision process is derived by aggregating only those non-conflicting opinions at each round of revision. In order to measure the consensus in a robust manner, a position-based dissimilarity measure is developed. Compared with the existing GDM models, the proposed GDM model is more effective and flexible in processing heterogeneous information. It can be used to handle different types of information with different degrees of granularity. Six types of information are exemplified in this paper, i.e., ordinal, interval, fuzzy number, linguistic, intuitionistic fuzzy set, and real number. The results indicate that the position-based consensus measure is able to overcome possible distortions of the results in large-scale GDM problems.
Language eng
DOI 10.1016/j.asoc.2015.03.055
Field of Research 089999 Information and Computing Sciences not elsewhere classified
0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
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Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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