Multi-period multi-attribute group decision-making under linguistic assessments

Abstract

In fuzzy environments, decision information is more suitable to be expressed in linguistic labels than exact numerical values. Group decision-making with linguistic assessments has received more and more attention over the last decades. Most research on this topic has focused on situations where all the original decision information is provided at the same time and refers to one and same period. However, in many decision areas, such as multi-period investment decision-making, medical diagnosis, personnel dynamic examination, military system efficiency dynamic evaluation, etc., the original decision information is usually collected at different periods and/or refers to different moments in time. This paper investigates the multi-period multi-attribute group decision-making problems where all decision information is expressed by decision-makers in multiplicative linguistic labels at different periods. The paper first introduces a new operator called a dynamic linguistic weighted geometric (DLWG) operator and uses the minimum variability model to derive the time series weights associated with the DLWG operator, and then utilises, respectively, the linguistic weighted geometric (LWG) operator and the DLWG operator to aggregate the given linguistic labels. Moreover, the paper develops an approach to multi-period multiple attribute group decision-making under linguistic assessments so as to derive the final ranking of alternatives, and finally, gives an illustrative example and extends the above results to uncertain linguistic environments.

Keywords:

• multi-period multi-attribute group decision-making
• unbalanced multiplicative linguistic label set
• aggregation
• dynamic linguistic weighted geometric operator
• time series

Keywords:

• 90B50
• 91B06

Acknowledgements

The author is very grateful to the anonymous referees for their insightful and constructive comments and suggestions that have led to an improved version of this paper. The work was supported by the National Science Fund for Distinguished Young Scholars of China (No.70625005).