Decision-making methods based on fuzzy soft competition hypergraphs

Abstract

Fuzzy soft set theory is an effective framework that is utilized to determine the uncertainty and plays a major role to identify vague objects in a parametric manner. The existing methods to discuss the competitive relations among objects have some limitations due to the existence of different types of uncertainties in a single mathematical structure. In this research article, we define a novel framework of fuzzy soft hypergraphs that export the qualities of fuzzy soft sets to hypergraphs. The effectiveness of competition methods is enhanced with the novel notion of fuzzy soft competition hypergraphs. We study certain types of fuzzy soft competition hypergraphs to illustrate different relations in a directed fuzzy soft network using the concepts of height, depth, union, and intersection simultaneously. We introduce the notions of fuzzy soft k-competition hypergraphs and fuzzy soft neighborhood hypergraphs. We design certain algorithms to compute the strength of competition in fuzzy soft directed graphs that reduce the calculation complexity of existing fuzzy-based non-parameterized models. We analyze the significance of our proposed theory with a decision-making problem. Finally, we present graphical, numerical, as well as theoretical comparison analysis with existing methods that endorse the applicability and advantages of our proposed approach.

On probabilistic linguistic term set operations

“…Many mathematical modelings have been presented recently to deal with randomness, fuzziness, vagueness and uncertainty of decision environment. To deal with the complexity of decision making, combining different theories together has emerged as an important trend, such as probabilistic rough sets [1,2], rough graphs [3][4][5], fuzzy soft graphs [6,7] and Hesitant fuzzy linguistic term sets [8,9].…”

Decision Making Based on Valued Fuzzy Superhypergraphs

“…In addition, the book also sheds light on real-world applications of these hypergraphs, making it a valuable resource for students and researchers in the field of mathematics, as well as computer and social scientists [17]. There is also some research about fuzzy (hyper) graphs and their applications in complex hypernetworks, such as the implementation of single-valued neutrosophic soft hypergraphs on the human nervous system [18], decision-making methods based on fuzzy soft competition hypergraphs [19], hypergraph and network flow-based quality function deployment [20], global domination in fuzzy graphs using strong arcs [21], fuzzy hypergraph modeling, analysis and prediction of crimes [22], single-valued neutrosophic directed (hyper) graphs and applications in networks [23], achievable single-valued neutrosophic graphs in wireless sensor networks [24], fuzzy hypergraph network for recommending top-k profitable stocks [25], an algorithm to compute the strength of competing interactions in the bearing sea based on Pythagorean fuzzy hypergraphs [26] and centrality measures in fuzzy social networks [27]. Recently, Smarandache extended hypergraphs to a new concept as nsuperhypergraph and Plithogenic n-superhypergraph which have several properties and are connected with the real-world [28].…”

A Fuzzy-Soft-Set Theoryapproach for Solving Decision-Making Problems on Certain Hybrid Set Model

“…Zulqarnain et al (2021) determined the correlation coefficient by the TOPSIS technique primarily and used for selection-making. Akram et al (2022) employed selection-making strategies based totally on fuzzy soft opposition hypergraphs. We created fuzzy soft hypergraphs, a singular framework that exports the features of fuzzy-soft-sets to hypergraphs.…”