Three Decades of Multiple Criteria Decision-Making (MCDM) Methods (1996–2026): A Comprehensive Review of Advancements, Applications, and Future Directions

Sushil Kumar Sahoo; Bibhuti Bhusan Choudhury; Prasant Ranjan Dhal; Sudhakar Majhi; Ipsita Dhar; Supriya Sahu

doi:10.67334/cds21202631

Authors

Sushil Kumar Sahoo 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela, Odisha, India Author https://orcid.org/0000-0002-8551-7353
Bibhuti Bhusan Choudhury 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela, Odisha, India Author https://orcid.org/0000-0002-2990-3911
Prasant Ranjan Dhal 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela, Odisha, India Author https://orcid.org/0000-0002-7402-2187
Sudhakar Majhi 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela, Odisha, India Author https://orcid.org/0000-0003-2213-8791
Ipsita Dhar 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela, Odisha, India Author https://orcid.org/0009-0007-4086-8137
Supriya Sahu 1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India; 2) Biju Patnaik University of Technology, Rourkela, Odisha, India Author https://orcid.org/0000-0002-1823-9568

DOI:

https://doi.org/10.67334/cds21202631

Keywords:

Multiple Criteria Decision-Making, Multi-Attribute Decision-Making, AHP, TOPSIS, Fuzzy MCDM, Hybrid Decision Models, Artificial Intelligence, Machine Learning, Bibliometric Analysis, Decision Support Systems

Abstract

Multiple Criteria Decision-Making (MCDM) techniques have proven to be indispensable tools for addressing decision problems involving multiple conflicting criteria across a wide range of applications. This review paper comprehensively examines the evolution, applications, and future directions of MCDM methods from 1996 to 2026. A systematic literature review was conducted using publications retrieved from multiple scientific databases, and the collected studies were analyzed through bibliometric and thematic approaches. The review highlights the growing importance of widely adopted methods such as AHP, TOPSIS, VIKOR, DEMATEL, ELECTRE, PROMETHEE, MOORA, COPRAS, MARCOS, and CoCoSo across diverse domains, including engineering, healthcare, robotics, renewable energy, transportation, and sustainability. Furthermore, hybrid frameworks integrating fuzzy logic, grey theory, machine learning, deep learning, and artificial intelligence have significantly enhanced decision-support capabilities under uncertainty. The study also identifies key research gaps and future directions related to computational complexity, scalability, interpretability, and real-time intelligent decision-making systems.

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