Criteria Weighting Methods in Multi-Criteria Decision Making: A Comprehensive Review of Subjective, Objective, and Hybrid Approaches

Arkyadeep Sarkar; Shankha Shubhra Goswami; Dhiren Kumar Behera; Darko Bozanic

doi:10.67334/cds202619

Authors

Arkyadeep Sarkar Department of Mechanical Engineering, Abacus Institute of Engineering and Management, Hooghly, India Author https://orcid.org/0000-0001-9475-1080
Shankha Shubhra Goswami Department of Mechanical Engineering, Abacus Institute of Engineering and Management, Hooghly, India Author https://orcid.org/0000-0002-0033-3089
Dhiren Kumar Behera Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, India Author https://orcid.org/0000-0002-2547-5518
Darko Bozanic Military Academy, University of Defence in Belgrade, Belgrade, Serbia Author https://orcid.org/0000-0002-9657-0889

DOI:

https://doi.org/10.67334/cds202619

Keywords:

Multi-Criteria Decision Making, MCDM, Subjective and Objective Weighting, Hybrid Weighting Approaches, Decision Support Systems, Criteria Weighting Methods

Abstract

In Multi-Criteria Decision Making (MCDM) method, the criteria weighting is one of the most critical parameters that affect the stability of the ranks, sensitivity of the decision and the robustness of the model. A variety of methods have been developed to weight; these can be categorized as subjective, objective and hybrid. The weights derived from the statistical or information theoretic properties of the inherent data are called objective methods, while those based on expert judgment and cognitive evaluations are called subjective methods. Combining both paradigms is known as hybrid methods and provides a more reliable and unbiased approach. Although significant methodological progress has been made, there remain many different methods, variations in method selection, validation and contextual applicability. The paper comprehensively reviews and critically analyzes the most important criteria weighting techniques in MCDM. It categorizes and compares current solutions, analyzes their theory and limitations, and explores their use in different fields including engineering, energy systems, supply chain and financial decision-making. The research also identifies the new trends like fuzzy logic integration, machine learning based weighting and adaptive decision frameworks. Thirdly, the main research gaps and future research directions are suggested to lead next generation weighting models in MCDM under uncertainty.

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