An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges

Due to the uncertainty of decision environment and differences of decision makers’ culture and knowledge background, multi-granular HFLTSs are usually elicited by decision makers in a multi-attribute group decision making (MAGDM) problem. In this paper, a novel consensus model is developed for MAGDM based on multi-granular HFLTSs. First, it is defined the group consensus measure based on the fuzzy envelope of multi-granular HFLTSs. Afterwards, an optimization model which aims to minimize the overall adjustment amount of decision makers’ preference is established. Based on the model, an iterative algorithm is devised to help decision makers reach consensus in MAGDM with multi-granular HFLTSs. Numerical results demonstrate the characteristics of the proposed consensus model.

Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach

Managing noncooperative behaviors in large-scale group decision-making: Integration of independent and supervised consensus-reaching models

Group decision making with heterogeneous intuitionistic fuzzy preference relations

In most of the consensus models for multi-attribute group decision making (MAGDM), the decision makers are required to provide evaluation information in given expression domains under the same evaluation attributes, which cannot well represent the heterogeneity of evaluation information and the differences of decision makers to meet the realistic requirements. In this work, we propose a consensus model for heterogeneous MAGDM with several attribute sets, in which each decision maker can independently choose evaluation attributes, set attribute weights, express evaluation information, and then decision makers can reach an agreement through a consensus reaching process. The key of the proposed model is to transform the individual heterogeneous evaluation attributes of each decision maker into corresponding two homogeneous evaluation attributes, i.e. the distance from the alternatives to the Positive Ideal Solution (PIS) and the distance from the alternatives to the Negative Ideal Solution (NIS). More specifically, every evaluation attribute of each decision maker is regarded as a small module. Then, the heterogeneous TOPSIS method is used to calculate the separation from NIS and proximity to PIS of alternatives in small modules and aggregate these distances under different attributes of the same alternative using attribute weights for each decision maker; then, consensus process and selection process are implemented via the new homogeneous evaluation attributes of alternatives for decision makers. Two case studies illustrate the effectiveness of the proposed model.

Guiqing Zhang

Papers

Minimum deviation ordinal consensus reaching in GDM with heterogeneous preference structures