The Application of Energy and Laplacian Energy of Hesitancy Fuzzy Graph Based on Similarity Measures in Decision Making Problems
This work addresses decision-making problems in uncertain environments, but it appears incremental as it builds on existing hesitancy fuzzy graph methods.
The authors tackled the problem of decision-making under uncertainty by defining a new hesitancy fuzzy similarity measure and developing an algorithm to estimate expert reputation scores using hesitancy fuzzy preference relationships, validated with real-time numerical examples.
In this article, a new hesitancy fuzzy similarity measure is defined and then used to develop the matrix of hesitancy fuzzy similarity measures, which is subsequently used to classify hesitancy fuzzy graph using the working procedure. We build a working procedure (Algorithm) for estimating the eligible reputation scores values of experts by applying hesitancy fuzzy preference relationships (HFPRs) and the usual similarity degree of one distinct HFPRs to each other's. As the last step, we provide real time numerical examples to demonstrate and validate our working procedure.