Group Decision Analysis Algorithms with EDAS for Interval Fuzzy Sets

Abstract The purpose of this paper is proposing, analyzing and assessing two new algorithms of the EDAS method for group multi-criteria decision making with fuzzy sets. In the first proposed EDAS extension for distance measure between two interval Type-2 fuzzy numbers is applied Graded Mean Integration Representation (GMIR). The second algorithm takes into account the proximity between the fuzzy alternatives and its similarity measure is Map Distance Operator (MDO). The two new algorithms are verified by a numerical example. Comparative analysis of obtained rankings demonstrates that GMIR extension is more reliable as an interval Type-2 fuzzy alternative to Evaluation based on Distance from Average Solution (EDAS). In case that time is of the essence, the MDO EDAS could be preferred.

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Multi-criteria decision-making method based on type-2 fuzzy sets

Filomat ◽

10.2298/fil1702431w ◽

2017 ◽

Vol 31 (2) ◽

pp. 431-450 ◽

Cited By ~ 8

Author(s):

Jing Wang ◽

Qing-Hui Chen ◽

Hong-Yu Zhang ◽

Xiao-Hong Chen ◽

Jian-Qiang Wang

Keyword(s):

Decision Making ◽

Fuzzy Sets ◽

Computational Models ◽

Fuzzy Numbers ◽

Multi Criteria Decision Making ◽

Interval Numbers ◽

Interval Type ◽

Uncertainty Information

Type-2 fuzzy sets (T2FSs) are the extension of type-1 fuzzy sets (T1FSs), which can convey more uncertainty information in solving multi-criteria decision-making (MCDM) problems. Motivated by the extension from interval numbers to triangular fuzzy numbers, three-trapezoidal-fuzzy-number-bounded type-2 fuzzy numbers (TT2FNs) are defined on the basis of interval type-2 trapezoidal fuzzy numbers (IT2TFNs), and they can convey more uncertainty information than T1FSs and IT2FSs. Moreover, the drawbacks of the existing computational models of generalized fuzzy numbers are analyzed, and a new computational model of fuzzy numbers is proposed, which is further extended to TT2FNs. Besides, a MCDM method is proposed to deal with the evaluation information given in the form of TT2FNs. Finally, an illustrative example and comparison analysis are provided to demonstrate the feasibility and validity of the proposed method.

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Group Decision Analysis with Interval Type-2 Fuzzy Numbers

Cybernetics and Information Technologies ◽

10.1515/cait-2017-0003 ◽

2017 ◽

Vol 17 (1) ◽

pp. 31-44 ◽

Cited By ~ 4

Author(s):

Galina Ilieva

Keyword(s):

Decision Analysis ◽

Fuzzy Sets ◽

Business Intelligence ◽

Fuzzy Numbers ◽

Group Decision ◽

Analysis Method ◽

Interval Type ◽

Benefits And Costs ◽

Selection Of

Abstract This paper presentsagroup multi-criteria DEMATELand VIKORdecision analysis method with interval type-2 fuzzy sets. In order to compare normal fuzzy trapezoidal numbers, we convert them into crisp values using graded mean integration representation. Byacase study for selection of business intelligence platform, we prove that the proposed combination isafeasible solution that can work with benefits and costs criteria, while also reducing uncertainty in experts’assessments.

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An interval type-2 hesitant fuzzy best-worst method

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202801 ◽

2021 ◽

pp. 1-28

Author(s):

Ashraf Norouzi ◽

Hossein Razavi hajiagha

Keyword(s):

Decision Making ◽

Fuzzy Sets ◽

Group Decision Making ◽

Group Decision ◽

Fuzzy Ahp ◽

Uncertain Data ◽

Interval Type ◽

Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

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