A Novel MADM Method based on Grey Preference Degree and its Application in Supplier Selection

Supratim Mukherjee; Amitava Chatterjee

doi:10.67334/cds31202643

Authors

Supratim Mukherjee Department of Mathematics, Government General Degree College, Tehatta, West Bengal, India Author https://orcid.org/0009-0004-2189-9438
Amitava Chatterjee Department of Mathematics, J.S.S.R.K. High School, Purba Bardhaman, West Bengal, India Author https://orcid.org/0000-0001-9046-8528

DOI:

https://doi.org/10.67334/cds31202643

Keywords:

Grey Systems, Interval Grey Numbers, MADM, Grey Preference Degree, TOPSIS

Abstract

Solving Multi-Attribute Decision-Making (MADM) problems under uncertainty is a challenging task, particularly in group decision-making environments where decision-makers may differ in their experience, expertise, and evaluation capabilities. Therefore, determining the appropriate weights of decision-makers is an important aspect of the decision-making process. This study proposes a three-phase group decision-making framework that addresses uncertainty using Interval Grey Numbers (IGNs). In the first phase, the weights of decision-makers are determined based on the deviations between their individual evaluations and predefined ideal decisions. In the second phase, a novel comparison measure, called the Grey Preference Degree, is introduced to compare IGNs and address the limitations of existing comparison approaches. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is then employed to evaluate and rank the alternatives. In the third phase, a classification procedure is developed to group alternatives according to their performance, supporting future decision-making activities. The proposed methodology is demonstrated through a practical supplier selection problem. A comparative analysis is conducted to examine its methodological advantages over existing approaches. The proposed framework integrates decision-maker weighting, alternative ranking, and classification into a unified decision-making process under uncertainty.

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