Name
Title | ||
|---|---|---|
Probabilistic Hesitant Fuzzy Taxonomy Method Based on Best–Worst-Method (BWM) and Indifference Threshold-Based Attribute Ratio Analysis (ITARA) for Multi-attributes Decision-Making |
Abstract | ||
|---|---|---|
The probabilistic hesitant fuzzy set (PHFS) is an important extension of hesitant fuzzy set (HFS), which can more accurately describe the uncertainty of elements and can show more flexibility of decision maker (DM) in the process of decision-making. Taxonomy method is a useful tool for grading, classifying, and comparing different activities with respect to their advantages and utility degree from studied attributes. In this paper, a probabilistic hesitant fuzzy Taxonomy method based on Best–Worst-Method (BWM) and Indifference Threshold-based Attribute Ratio Analysis (ITARA) for multi-attributes decision-making (MADM) is presented. First, the definitions about PHFS and probabilistic hesitant fuzzy element (PHFE) are introduced, and some corresponding operations are given. Second, we adopt BWM to obtain subjective weights and ITARA approach to obtain objective weights, by combining the subjective weights and objective weights together; we can derive the final weights. Furthermore, we extend the Taxonomy method to the PHFS. Finally, an example of selecting research topic is given to illustrate the usefulness of our proposed method. Compared with other useful methods, the validity and superiority of our proposed method can be showed. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s40815-021-01206-7 | International Journal of Fuzzy Systems |
Keywords | DocType | Volume |
Probabilistic hesitant fuzzy set, BWM, ITARA, Taxonomy, MADM | Journal | 24 |
Issue | ISSN | Citations |
3 | 1562-2479 | 0 |
PageRank | References | Authors |
0.34 | 24 | 3 |