Group decision making based on consistency and consensus analysis of dual multiplicative linguistic preference relations

被引:16
|
作者
Meng, Fanyong [1 ,3 ]
Chen, Shyi-Ming [2 ]
Fu, Linxian [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Management Sci & Engn, Nanjing 210044, Peoples R China
[2] Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan
[3] Cent South Univ, Sch Business, Changsha 410083, Peoples R China
关键词
Decision analysis; Dual multiplicative linguistic variable; Preference relation; Interactive algorithm; Consistency and consensus; AGGREGATION OPERATORS; FUZZY-SETS; SELECTION; MODEL;
D O I
10.1016/j.ins.2021.05.056
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a new group decision making (GDM) method based on the consistency and the consensus analysis of dual multiplicative linguistic preference relations (DMLPRs). A new type of linguistic variables, called dual multiplicative linguistic variables (DMLVs), is presented, which is defined on the multiplicative linguistic scale. DMLVs are used to rep-resent asymmetrical qualitative hesitancy judgments of decision makers (DMs). A maximum-consistency-based interactive algorithm to derive multiplicative linguistic intu-itionistic preference relations (MLIPRs) is presented, by which the consistency concept for DMLPRs is obtained. Then, we define the concept of inconsistent DMLPRs and propose an optimal-model-based method for deriving consistent DMLPRs. Furthermore, incomplete DMLPRs also can be dealt with by the proposed maximum-consistency-based interactive algorithm. For GDM, the weights of DMs are determined by the cosine-based correlation coefficient between individual DMLPRs. Moreover, we propose a consensus measure to cal-culate the agreement degree of DMLPRs and build an optimal model to increase the con -sensus level of individual DMLPRs. Finally, a new GDM method (call Algorithm III) is offered and an application example is used to illustrate the proposed GDM method. The proposed GDM method outperforms the former GDM methods for GDM in the environ-ments of DMLPRs. (c) 2021 Elsevier Inc. All rights reserved.
引用
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页码:590 / 610
页数:21
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