The Complex Neutrosophic Soft Expert Relation and Its Multiple Attribute Decision-Making Method

Area of Science:

• Soft Computing
• Decision Science
• Data Analysis

Background:

• Handling imprecise, uncertain, and indeterminate data is challenging.
• Existing models struggle to incorporate expert opinions and time factors in decision-making.
• Complex neutrosophic soft expert sets (CNSESs) offer a framework for representing complex, multi-dimensional data.

Purpose of the Study:

• Introduce a novel soft computing technique, the complex neutrosophic soft expert relation (CNSER).
• Evaluate the interaction degree between complex neutrosophic soft expert sets (CNSESs).
• Develop a method suitable for time-sensitive decision-making problems.

Main Methods:

• Definition of complex neutrosophic soft expert sets (CNSESs) and complex neutrosophic soft expert relations (CNSERs).
• Development of an algorithm to determine the type and degree of relationships.
• Empirical study on currency exchange rate variability and Malaysian exports using CNSER.
• Introduction of inverse, complement, composition, symmetry, transitivity, reflexivity, equivalence relations, and equivalence classes for CNSERs.

Main Results:

• CNSER effectively evaluates the interaction degree between CNSESs.
• An empirical study demonstrated the relationship between currency exchange rate variability and Malaysian exports over time.
• The proposed CNSER showed superiority compared to existing relations.
• Properties like inverse, complement, composition, and equivalence relations for CNSERs were defined and analyzed.

Conclusions:

• CNSER is a powerful tool for analyzing complex, uncertain data in time-sensitive decision-making.
• The method provides a robust framework for understanding inter-variable relationships, as shown in the Malaysian export case study.
• Further theoretical properties and applications of CNSER can be explored.