A Comprehensive Review: The Novel Weighting Methods For Multi-Criteria Decision-Making (MCDM)

Arkyadeep Sarkar; Shankha Shubhra Goswami

doi:10.31181/sor202781

Authors

Arkyadeep Sarkar Department of Mechanical Engineering, Abacus Institute of Engineering and Management, Hooghly, India Author https://orcid.org/0000-0002-4597-4904
Shankha Shubhra Goswami Department of Mechanical Engineering, Abacus Institute of Engineering and Management, Hooghly, India Author https://orcid.org/0000-0002-0033-3089

DOI:

https://doi.org/10.31181/sor202781

Keywords:

Multi-Criteria Decision-Making, MCDM, Novel weighting methods, Hybrid approaches, Fuzzy and uncertainty modeling, Artificial intelligence in decision making, Criteria weighting techniques

Abstract

In Multi-Criteria Decision-Making (MCDM), the importance of the criteria, interpreted relative to each other, defines a key factor that directly determines the accuracy, transparency, and reliability of the final assessment. In the past ten years, there have been tremendous changes in the fields of computational intelligence, uncertainty modelling, and multi-faceted decision frameworks, and the number of novel weighting approaches has gone beyond the constraints of traditional subjective and objective models. This paper provides an in-depth overview of these new methods, including current subjective schemes, objective models derived from data, integrations, fuzzy and probabilistic developments, and artificial intelligence weighting schemes. The review identifies how these methods help enhance robustness, minimize bias, improve uncertainty management, and enable flexibility when faced with complex situations by analyzing peer-reviewed articles published between 2010 and 2025. Comparative reflections are used to identify the methodological strengths, practical limitations, and implementation issues of each group of these weighting strategies. Another important area is the increasing popularity of explainability, universal benchmarking, and big-data integration as key future trends, which are highlighted in the review. On balance, the current research summarizes the dynamic nature of weighting procedures and contributes useful insights regarding their use by researchers, practitioners, and policymakers in search of more reliable and intelligent decision support systems.

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