There is a newer version of the record available.

Published October 1, 2025 | Version v1
Journal article Open

Multineutrosophic Analysis with the ARAS Method for Selecting Trading Strategies

Description

Deciding on optimal business solutions is a perennial problem for firms challenged by ever-changing market environments. Currently, the choice of a solution entails multidimensional requirements in addition to similar uncertainties and vagueness factoring human fallibility and ever-challenging market conditions. Even though thousands of systems for decision-making support exist, the dedicated scholarly literature does not support any advancement that would ease an all-encompassing evaluation of strategic solutions during complete uncertainty or information vagueness. Therefore, this research aims to fill this theoretical gap by creating a new approach based on practical application. This new approach is based on the employment of the Additive Ratio Assessment method (the ARAS method) in a multineutrosophic setting to give better weighting to criteria and simultaneously assess strategic solutions. Multineutrosophic refers to a level of truth, falsity, and indeterminacy. Thus, by applying neutrosophic operators, the assessments of various experts are comprehensively applied to turn qualitative attributes into quantitative, highly reliable values, leading to appropriate results through the multineutrosophic ARAS method to assess degrees of effectiveness of business solutions to rank potential business strategies for ultimate success through widespread uncertainty. This work contributes theoretically and practically: 1. theoretically expands the scope of multi-criteria decision-making processes by formally incorporating the element of uncertainty; 2. practically provides firms with a new tool for conducting strategic assessments as it will increase efficiency and effectiveness of resource usage while decreasing vulnerability to inevitable downturns in the marketplace.

Files

13.MultineutrosophicAnalysisARAS.pdf

Files (627.6 kB)

Name Size Download all
md5:3667e85ddcae9daf235005ce18aa578d
627.6 kB Preview Download