Journal
APPLIED SOFT COMPUTING
Volume 60, Issue -, Pages 30-47Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2017.06.028
Keywords
Group decision-making (GDM); Multiplicative linguistic preference relations (MLPRs); Fuzzy information granulation (fuzzy IG); Consistency measure; Consensus reaching process (CRP)
Categories
Funding
- Anhui Provincial Natural Science Foundation [1708085MG168]
- National Natural Science Foundation of China [71171112, 71601002]
- Ministry of Education of Humanities and Social Science Project of China [16YJC630077]
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An adaptive consensus model based on fuzzy information granulation (fuzzy IG) is presented for group consensus decision-making problems with multiplicative linguistic preference relations (MLPRs). Firstly, a granular representation of linguistic terms is concerned with the triangular fuzzy formation of a family of information granules over given Analytical Hierarchy Process (AHP) numerical scales. On this basis, the individual consistency and group consensus measure indices using fuzzy granulation technique are constructed, respectively. Then, the optimal cut-off points of fuzzy information granules are obtained by establishing a multi-objective optimization model together with a multi-objective particle swarm optimization (MOPSO) algorithm. A novel group consensus decision-making approach where consensus reaching process (CRP) is achieved by adaptively adjusting individual preferences through the optimization of the cut-off points is proposed. After conflict elimination, the obtained group preference gives the ranking of the alternatives. Finally, a real emergency decision-making case for liquid ammonia leak is given to illustrate the application steps of the proposed method and comparative analysis with the existing GDM methods. Comparative results demonstrate that the proposed method has some advantages in aspects of avoiding information loss or distortion and improving consensus performance. (C) 2017 Elsevier B.V. All rights reserved.
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