A Comprehensive Review of Fuzzy Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions

Sushil Kumar Sahoo; Bibhuti Bhusan Choudhury; Prasant Ranjan Dhal

doi:10.31181/sdmap41202764

Authors

Sushil Kumar Sahoo Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang (BPUT), Odisha, India Author https://orcid.org/0000-0002-8551-7353
Bibhuti Bhusan Choudhury Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang (BPUT), Odisha, India Author https://orcid.org/0000-0002-2990-3911
Prasant Ranjan Dhal Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang (BPUT), Odisha, India Author https://orcid.org/0000-0002-7402-2187

DOI:

https://doi.org/10.31181/sdmap41202764

Keywords:

Fuzzy MCDM, Multiple Criteria Decision-Making, MCDM, Fuzzy logic, Uncertainty modeling, Fuzzy AHP, Fuzzy TOPSIS, Hybrid decision-making methods, Intuitionistic fuzzy sets, Pythagorean fuzzy sets

Abstract

Real-world situations can be associated with numerous conflicting criteria, imprecise information, and subjective human decisions. Traditional Multiple Criteria Decision-Making (MCDM) approaches are not able to manage such uncertainty sufficiently. MCDM methods based on fuzzy logic address these shortcomings by incorporating linguistic preferences and modelling uncertainty in expert appraisal. In this paper, fuzzy MCDM techniques are reviewed thoroughly, tracing their development from classical fuzzy extensions to more recent developments in intuitionistic, Pythagorean, and picture fuzzy models. The paper offers a systematic classification, including outranking, value-based, pairwise comparison, and hybrid decision models. Important application areas such as energy planning, healthcare, supply chain management, transportation, and intelligent systems are critically scrutinized. Challenges related to computational complexity, subjectivity, model validation, and real-time deployment are addressed. Finally, future directions are identified, including intelligent automation, data-driven decision support, standardization of uncertainty modelling, and autonomous decision-making in dynamic environments.

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