The optimal group consensus models for 2-tuple linguistic preference relations

被引:53
作者
Gong, Zai-Wu [1 ]
Forrest, Jeffrey [2 ]
Yang, Ying-Jie [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Coll Econ & Management, Nanjing 210044, Jiangsu, Peoples R China
[2] Int Inst Gen Syst Studies, Grove City, PA 16127 USA
[3] De Montfort Univ, Sch Comp, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England
基金
中国国家自然科学基金;
关键词
Group decision making; Consensus measure; 2-Tuple linguistic; Convergence; Combination matrix; GROUP DECISION-MAKING; REPRESENTATION MODEL; TERM SETS; FUZZY; INFORMATION; ASSESSMENTS; MAJORITY; CONSISTENCY; FORECASTS; OPERATORS;
D O I
10.1016/j.knosys.2012.09.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We establish in this paper the optimization model of group consensus of 2-tuple linguistic preferential relations (LPRGCO Model), put forward three kinds of solutions to this model, and discover in it the convergence of group consensus. To detect the LPRGCO Model, we first build two kinds of optimal matrices as standards to measure the group consensus of 2-tuple linguistic preference relations (LPRs). And to analyze consensus deviations, we then adopt three types of measures, namely, the individual degree of consistency regarding alternative decision pairs, the deviational degree of the group consensus regarding alternative decision pairs, and the degree of group consensus regarding the original 2-tuple LPRs. On the basis of the previous analysis we not only construct an optimization model to probe into the deviation of the group consensus of 2-tuple LPRs by minimizing the weighted arithmetic average of deviation degrees of individual consistency, but also point out three feasible solutions to this optimization model: the optimal solution, satisfactory solutions, and non-inferior solutions. Accordingly, we discover different conditions in terms of the three solutions. And hence, we can from the aforementioned discussion draw a conclusion that the deviation of group consensus either decreases or stays invariant as the number of decision makers (DM) increases. To expatiate on the practical value of the model proposed, we will display in this paper numerical examples. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:427 / 437
页数:11
相关论文
共 39 条
[1]   COMBINATION OF FORECASTS [J].
BATES, JM ;
GRANGER, CWJ .
OPERATIONAL RESEARCH QUARTERLY, 1969, 20 (04) :451-&
[2]  
Ben-Arieh David., 2006, Fuzzy Optimization and Decision Making, V5, P371
[3]   COMBINING FORECASTS [J].
BUNN, DW .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1988, 33 (03) :223-229
[4]   Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information [J].
Cabrerizo, F. J. ;
Perez, I. J. ;
Herrera-Viedma, E. .
KNOWLEDGE-BASED SYSTEMS, 2010, 23 (02) :169-181
[5]   A CONSENSUS MODEL FOR GROUP DECISION MAKING PROBLEMS WITH UNBALANCED FUZZY LINGUISTIC INFORMATION [J].
Cabrerizo, F. J. ;
Alonso, S. ;
Herrera-Viedma, E. .
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, 2009, 8 (01) :109-131
[6]  
Chen H. Y., 2001, FORECASTING, V20, P72
[7]  
Chen Hua-you, 2003, Journal of Systems Engineering, V18, P203
[8]   Integration of a consistency control module within a consensus model [J].
Chiclana, Francisco ;
Mata, Francisco ;
Martinez, Luis ;
Herrera-Viedma, Enrique ;
Alonso, Sergio .
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2008, 16 (01) :35-53
[9]   Combining numerical and linguistic information in group decision making [J].
Delgado, M ;
Herrera, F ;
Herrera-Viedma, E ;
Martinez, L .
INFORMATION SCIENCES, 1998, 107 (1-4) :177-194
[10]   A communication model based on the 2-tuple fuzzy linguistic representation for a distributed intelligent agent system on Internet [J].
M. Delgado ;
F. Herrera ;
E. Herrera-Viedma ;
M. J. Martin-Bautista ;
L. Martínez ;
M. A. Vila .
Soft Computing, 2002, 6 (5) :320-328