Multiple-criteria decision making method based on the scaled prioritized operators with unbalanced linguistic information

Abstract

The unbalanced linguistic terms set (ULTS) is a special linguistic term set which can describe the vagueness assessment that is non-uniform and non-symmetrical distributed. So, it is effective to describe the uncertainty information existed in some special real decision making problems by ULTS. As a special prioritized operator, the scaled prioritized (SP) operator has the advantage of taking the priority among different criteria into account by detailed priority labels in known case and unknown case. In this paper, we combine the merits of SP operators and ULTS for dealing with some special multi-criteria decision making (MCDM) problems where there is a priority relationship between criteria under ULTS evaluation information. We present the unbalanced 2-tuple linguistic scaled prioritized averaging operator and the unbalanced 2-tuple linguistic scaled prioritized geometric averaging operator, which can handle the issues of the detailed priority relationship among different categories of MCDM problems in knowable case. Further, we propose the unbalanced 2-tuple linguistic scaled prioritized weighted averaging operator and the unbalanced 2-tuple linguistic scaled prioritized geometric weighted averaging operator, which can deal with the case when the detailed priority relationship among different categories of different criteria is unknowable. Then, we discussed several characteristics of the proposed operators, such as boundedness, monotonicity, and idempotency. Besides, we presented an approach for the MCDM problems according to the proposed operators. In the last, we provide an example to explain the calculating steps and effectiveness of these methods.