A New Distance-Based Consensus Reaching Model for Multi-Attribute Group Decision-Making with Linguistic Distribution Assessments

Abstract

A B S T R A C TThis paper proposes a novel consensus reaching model for multi-attribute group decision making (MAGDM) with information represented by means of linguistic distribution assessments. Firstly, some drawbacks of the existing distance measures for linguistic distribution assessments are analyzed by using numerical counterexamples, and a new distance measure is proposed for linguistic distribution assessments in order to alleviate the limitations. Then, a novel consensus reaching model is developed for MAGDM with linguistic distribution assessment, in which a feedback mechanism is devised by combining an identification rule and an optimization-based model. In this consensus framework, the model allows experts who are identified to modify their preferences to provide additional preference information about linguistic distribution assessments in each iteration. Meanwhile, by solving an optimization model, the consensus reaching model can automatically generate preference adjustment suggestions for experts. Moreover, the optimization model solved in each iteration minimizes the deviation between the adjusted values and initial preferences, which in turn leads to the good performance of the proposed consensus reaching model in preserving the initial preference information. Finally, an illustrative example shows that the proposed consensus reaching model is feasible and effective, and a comparative analysis highlights the advantages and characteristics of the model.

An Influence Network-Based Consensus Model for Large-Scale Group Decision Making with Linguistic Information

“…k , k = 0, 1, … , g be two given LDAs on linguistic term set S. The distance measure between d 1 and d 2 is defined as [51]:…”

Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence

“…To start with, in Table 2 we provide a summary of these distance measurements (refer to column 4), which are the bases to support decision making. Several methods for distance measurements in LDs were reported (see [33], [97], [107], [119]).…”

Consensus Reaching Process in the Two-Rank Group Decision-Making with Heterogeneous Preference Information

“…The decision makers d 5 and d 6 use the additive preference relation P (5) and P (6) to express preference information on X, i.e., P…”

“…In real applications of GDM problems, due to the differences of the professional knowledge, cultural background, and social experience among different individuals, it is natural that different decision makers may utilize heterogeneous representation structures to express their preferences on the alternatives [3]. Recently, the study on consensus reaching process (CRP) has become an interesting and necessary area [4][5][6][7].…”