Double Fuzzy Probabilistic Interval Linguistic Term Set and a Dynamic Fuzzy Decision Making Model based on Markov Process with tts Application in Multiple Criteria Group Decision Making

TL;DR

This paper introduces a novel fuzzy probabilistic interval linguistic term set and a dynamic decision-making model using Markov processes, enhancing group decision accuracy in multi-criteria scenarios.

Contribution

It proposes the double fuzzy probability interval linguistic term set (DFPILTS), along with new algorithms and a dynamic weight determination method for improved group decision making.

Findings

Effective aggregation of linguistic evaluations demonstrated.

Dynamic attribute weights improve decision consistency.

Application to financial risk decision shows practical utility.

Abstract

The probabilistic linguistic term has been proposed to deal with probability distributions in provided linguistic evaluations. However, because it has some fundamental defects, it is often difficult for decision-makers to get reasonable information of linguistic evaluations for group decision making. In addition, weight information plays a significant role in dynamic information fusion and decision making process. However, there are few research methods to determine the dynamic attribute weight with time. In this paper, I propose the concept of double fuzzy probability interval linguistic term set (DFPILTS). Firstly, fuzzy semantic integration, DFPILTS definition, its preference relationship, some basic algorithms and aggregation operators are defined. Then, a fuzzy linguistic Markov matrix with its network is developed. Then, a weight determination method based on distance measure and information entropy to reducing the inconsistency of DFPILPR and obtain collective priority vector based on group consensus is developed. Finally, an aggregation-based approach is developed, and an optimal investment case from a financial risk is used to illustrate the application of DFPILTS and decision method in multi-criteria decision making.