A Graded Soft Set Framework for Multi-Criteria Decision-Making Under Uncertainty: Weighted Ranking and Parameter Reduction

Publication Details

Publication Date

10/11/2026

Volume / Issue

Vol 1, Issue 6 (2026)

Article No.

061

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-GTB5NFWA

Received

28 Oct 2026

Views

53

Downloads

17

Abstract

Decision-making under uncertainty is often complex and involves incomplete and imprecise information on several criteria. To address these challenges, this study introduces a graded soft set framework by incorporating membership and complementary inverse membership functions into soft set theory. Basic set operations and their algebraic properties are set up. Three algorithms are developed to solve the alternative selection problem, the weighted ranking problem and the parameter reduction problem. The proposed approaches use criterion weights and the grading scores for the evaluation of the alternatives and the determination of the less significant parameters. Applications to supplier selection and symptom-based assessment illustrate the framework's practical utility. The suggested methodology provides a straightforward and interpretable decision-supporting tool for multi-criteria decision-making, which needs to be validated in sensitivity analysis and compared to other existing methodologies.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.