Similarity-based generalized prioritized information fusion algorithm with T-operator for solving decision making problems

Prioritized Information Fusion Algorithm (PIFA) was introduced to cater the problem of fuzzy fusion information (FFI) in decision-making problems. However, in the existing approach, at most only two criteria were considered in the computation with specific operators. This will limit its application...

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Bibliographic Details
Main Authors: Mohamad, D., Rahin, N. S. N.
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Criteria
Data integration
Decision making
Operators
Similarity
Similarity measures
Online Access:Get full text
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Summary:Prioritized Information Fusion Algorithm (PIFA) was introduced to cater the problem of fuzzy fusion information (FFI) in decision-making problems. However, in the existing approach, at most only two criteria were considered in the computation with specific operators. This will limit its application for solving real decision-making problems and a lack of consideration in a larger number of criteria will produce a less favorable result. An addition of multi-subcriteria into the minor sub-criteria in FFI will be a significant improvement to the algorithm. In this paper, the said improvement is carried out to the PIFA with the use of similarity measures and T-operators. The similarity measure acts as a tool to find the level of similarity between two fuzzy numbers while the t-operators act as an aggregation instrument to aggregate a collection of data from multiple inputs to yield a single output. Two types of similarity measure which are the set-theoretic and the distance-based similarity measures are used with the T-operators of the algebraic product and Gassert’s T-operator was utilized. A case study on the evaluation of automobiles preferences is given as an illustration of the proposed algorithm.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0152683