Quantum mechanics

Linguistics

Process (computing)

Engineering

Philosophy

Statistics

Social psychology

Operating system

Selection (genetic algorithm)

Economics

Computer science

Bounded function

Management science

Natural language processing

Physics

Organic chemistry

Psychology

Mathematics

Group decision-making

Mathematical analysis

Artificial intelligence

Confidence interval

Mathematical economics

Granular computing

Chemistry

Rule-based machine translation

Term (time)

Fuse (electrical)

Group (periodic table)

Rough set

Electrical engineering

Information Fusion

In group decision making problems, there exist the situations that decision makers may use unbalanced linguistic term sets that are not uniformly and symmetrically distributed to provide their linguistic assessments over alternatives. Moreover, due to the difference in knowledge and culture backgrounds, it is also possible that multi-granular linguistic term sets may also be used by decision makers. How to manage multi-granular unbalanced linguistic information in consensus-based group decision making has becoming an important topic in linguistic decision making. In this paper, we first revise Herrera’s unbalanced linguistic term sets and propose a simplified linguistic computational model to fuse multi-granular unbalanced linguistic terms. Afterwards, for multi-criteria group decision making problems with multi-granular unbalanced linguistic information, we develop two optimization models to generate adjustment advice for decision makers who have to change his/her opinions in consensus reaching process, which consider both the bounded confidence levels and minimum adjustment of decision makers’ linguistic assessments. Moreover, an algorithm is further proposed to help decision makers reach consensus in group decision making. Eventually, an application example for ERP system supplier selection and some simulation results are presented to illustrate and justify the consensus reaching algorithm.

Consensus reaching for group decision making with multi-granular unbalanced linguistic information: A bounded confidence and minimum adjustment-based approach