In innumerable real-world challenges, observed values of data are often inaccurate or vague because of incomplete and/or non–obtainable information. In order to handle the impreciseness in data, uncertainty modeling plays a vital role by creating a simulation of the decision-making process of humans when data is incomplete or inaccurate. Fuzzy sets, originally introduced by Zadeh in 1965, are a useful tool to capture the imprecision and uncertainty in various problems. It is characterized by a membership degree between zero and one, and a non-membership degree which is equal to one minus the membership degree. Grattan-Guinness, Jahn, and Sambuc individually familiarized interval-valued fuzzy sets in which the set membership is treated as an interval. Although these concepts can handle incomplete information in various real-world issues, they cannot address all types of uncertainty such as indeterminate and inconsistent information. Therefore, the intuitionistic fuzzy set (IFS) theory launched by Atanassov, addresses the problem of uncertainty by considering a non-membership function along with the fuzzy membership function on a universal set. The membership degree of an object is complemented with a non-membership degree that gives the extent to which an object does not belong to the IFS such that the sum of the two degrees should be less than or equal to 1.

However, this concept has its shortcomings in the handling of indeterminacy. Recently, research on uncertainty and indeterminacy modeling is progressing rapidly and many necessary and breakthrough studies have already been done and some extension of fuzzy sets such as picture fuzzy set, Pythagorean fuzzy set, spherical fuzzy set, neutrosophic set, plithogenic set, and their generalizations have been proposed. For example, neutrosophic theory that was founded by Smarandache in 1998 constitutes a further generalization of fuzzy set, intuitionistic fuzzy set, picture fuzzy set, Pythagorean fuzzy set, spherical fuzzy set, etc. In 2013, Yager introduced the Pythagorean fuzzy set, and the plithogenic set (as a generalization of crisp, fuzzy, Intuitionistic fuzzy and Neutrosophic sets) was introduced by Smarandache in 2017. The plithogenic set is a set with elements characterized by attribute values.

The objective of this Special Issue is to compile recent developments in methodologies, techniques, and applications of fuzzy set and its extensions for various practical problems and demonstrate the challenging issues within these concepts. We welcome authors to present state-of-the-art advancements in fuzzy set and its extensions techniques, methodologies, mixed approaches, and research directions focusing on unsolved issues.

Potential topics include but are not limited to the following:

• Decision models for knowledge sharing.
• Business analytics.
• Soft computing.
• Economic decisions with knowledge-based systems.
• Enterprise knowledge computing and evaluation.
• Strategic decision making.
• Decision models for learning.
• Intelligent decision-making.
• Intelligent optimization.
• Nature-inspired optimization.
• Data envelopment analysis.
• Supply chain management.
• Inventory, logistics, and transportation.
• Linear/ nonlinear systems.
• Computational modelling.