Abstract
Fuzzy set theory and its extended form have been widely used in multiple-attribute group decision-making (MAGDM) problems, among which the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) got a lot of attention for its ability of capturing information denoted by interval values. Based on the previous studies, to find a better solution for fusing qualitative quantization information with fuzzy numbers, we propose a novel definition of interval-valued q-rung orthopair uncertain linguistic sets (IVq-ROULSs) based on the linguistic scale functions, as well as its corresponding properties, such as operational rules and the comparison method. Furthermore, we utilize the power Muirhead mean operators to construct the information fusion method, and provide a variety of aggregation operators based on the proposed information description environment. A model framework is constructed for solving the MAGDM problem utilizing the proposed method. Finally, we illustrate the performance of the new method and investigate its advantages and superiorities through comparative analysis.
Highlights
multiple-attribute group decision-making (MAGDM) is essentially a process of making choices from a set of alternatives based on multiple decision makers’ (DMs’) evaluations under several attributes, whose methods and theories have been rapidly development in the past few decades [1,2,3,4,5,6,7,8,9,10]
Based on the above analysis, the novelty can be attributed to the following: First, to improve the applicability of q-rung orthopair fuzzy sets (q-ROFSs) in a highly complex and uncertain decision-making environment, this paper proposes a novel MAGDM information expression method based on IVq-ROULSs
We first proposed the concept of IVq-ROULSs by combining IVq-ROFSs with uncertain linguistic values (ULVs)
Summary
MAGDM is essentially a process of making choices from a set of alternatives based on multiple decision makers’ (DMs’) evaluations under several attributes, whose methods and theories have been rapidly development in the past few decades [1,2,3,4,5,6,7,8,9,10]. As the complexity of real-world problems increases, DMs’ expressions of evaluation decisions are becoming more and more diversified and complicated. How to describe the evaluation information of DMs more accurately has become a topic deserving research. In response to this problem, a widely accepted approach is to apply fuzzy theory [11] to MAGDM problems, by fuzzifying and defuzzifying the uncertain semantic contexts, the complex semantic information can be included as comprehensively as possible.
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