Multi-Attribute Decision-Making (MADM) methods often yield inconsistent rankings of alternatives due to their distinct computational mechanisms, complicating the selection of optimal solutions. This study proposes a novel grey-based aggregation framework to systematically resolve such inconsistencies.
The framework employs Grey Systems Theory to quantify ranking uncertainties through interval grey numbers. A Python-coded algorithm compares these grey numbers using possibility degree functions, enabling systematic aggregation of divergent rankings. The approach is demonstrated through a supplier selection case study combining results from nine MADM techniques.
The framework effectively resolves ranking conflicts while preserving the strengths of individual MADM methods. Computational results validate its ability to produce stable, consensus rankings from inconsistent inputs.
Organizations can apply this approach to integrate diverse MADM recommendations in complex decisions like sustainability assessment, supply chain management, resource allocation, healthcare systems or performance evaluation, particularly where methods disagree.
While MADM methods each offer unique advantages in handling specific decision scenarios, their inconsistent outcomes create practical challenges. This research provides a GST-based solution that computationally reconciles these differences through grey number comparison, implemented via Python for practical application.
