Globular T-Spherical Fuzzy (G-TSF) Sets for Multi-Criteria Group Decision-Making

Concetti Chiave

G-TSF Sets enhance decision-making with innovative extensions and operations.

Sintesi

The content introduces Globular T-Spherical Fuzzy (G-TSF) Sets as an extension of existing fuzzy set models. It discusses the representation of membership, indeterminacy, and non-membership degrees using a sphere, enabling a more accurate portrayal of vague information. The article covers basic set operations, algebraic operations for G-TSF Values, similarity measures, weighted aggregation operators, and a Multi-Criteria Group Decision-Making scheme for G-TSFSs. The proposed method is applied to select the best venue for professional development training sessions in a firm.

Statistiche

G-TSFSs represent membership, indeterminacy, and non-membership degrees using a globular/sphere bound.
Hamming distance and Euclidean distance are introduced for G-TSFSs.
TSFSs enable assignment of any value to membership degrees within [0,1].

Citazioni

Approfondimenti chiave tratti da

On Globular T-Spherical Fuzzy (G-TSF) Sets with Application to G-TSF Multi-Criteria Group Decision-Making

by Miin-Shen Ya... alle arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07010.pdf

Domande più approfondite

How can the concept of G-TSFSs be applied in real-world decision-making scenarios

G-TSFSs can be applied in real-world decision-making scenarios by providing a more accurate representation of vague, ambiguous, and imprecise information. In practical terms, this means that decision-makers can use G-TSFSs to evaluate objects within a flexible region defined by a sphere with specific center and radius. This enhanced evaluation capability allows for a more comprehensive assessment of various factors involved in decision-making processes. For example, in the context provided above regarding multi-criteria group decision-making for selecting the best venue for professional development training sessions in a firm, G-TSFSs can help weigh different criteria effectively and make informed decisions based on membership degrees, indeterminacy degrees, and non-membership degrees represented by globular/sphere bounds.

What are the potential limitations or drawbacks of using G-TSFSs in complex decision-making processes

While G-TSFSs offer significant advantages in decision-making processes due to their ability to handle uncertainty and ambiguity effectively, there are potential limitations or drawbacks as well. One limitation could be the complexity involved in determining the appropriate values for DoM, DoI, and DoN within the constraints set by G-TSFS definitions. Decision-makers may face challenges in assigning these values accurately across multiple criteria or dimensions when dealing with large datasets or complex scenarios. Additionally, interpreting results from G-TSFS calculations might require specialized knowledge or expertise in fuzzy set theory which could pose difficulties for users unfamiliar with these concepts.

How can the principles behind G-TSFSs be adapted to other fields outside mathematics

The principles behind G-TSFSs can be adapted to other fields outside mathematics such as artificial intelligence (AI), machine learning (ML), data science, and cognitive computing. By leveraging the concept of representing uncertainty using spheres with specific centers and radii as seen in G-TSF models, AI systems can make more nuanced decisions based on imprecise information similar to human reasoning processes. In ML applications like pattern recognition or anomaly detection where uncertainties exist in data patterns or classifications, incorporating G-TSF principles could enhance model accuracy and robustness against noise or outliers. Furthermore,
the adaptability of these principles opens up possibilities for implementing advanced decision support systems across various industries including healthcare,
finance,
and environmental management where precise yet uncertain information plays a crucial role
in strategic planning
and resource allocation.