Sensitivity Analysis and Validation in MCDM Methods: A Comprehensive Review with Advancements, Applications, and Future Directions

Arkyadeep Sarkar; Shankha Shubhra Goswami; Krishna Kumar Gupta

doi:10.31181/sdmap41202769

Authors

Arkyadeep Sarkar Department of Mechanical Engineering, Abacus Institute of Engineering and Management, India Author https://orcid.org/0009-0001-8274-4104
Shankha Shubhra Goswami Department of Mechanical Engineering, Abacus Institute of Engineering and Management, India Author https://orcid.org/0000-0002-0033-3089
Krishna Kumar Gupta Department of Mechanical Engineering, Abacus Institute of Engineering and Management, India Author https://orcid.org/0000-0001-9569-2385

DOI:

https://doi.org/10.31181/sdmap41202769

Keywords:

Multi-criteria decision-making, Weighting methods, Hybrid data-driven weights, Machine learning–based weighting, Uncertainty modelling, Fuzzy and entropy techniques, MCDM

Abstract

The accelerated development of multi-criteria decision-making (MCDM) tools in engineering, management, environmental planning, and financial analytics has increased the demand for more precise and responsive weighting methods. Stereotypical subjective and objective weighting methods tend to be biased, inflexible, or fail to react to dynamic decision settings. The review summarizes a series of newly developed novel weighting approaches, such as hybrid, data-driven, machine-learning-assisted, and uncertainty-adaptive approaches, to address these limitations. Drawing from a broad scope of published literature, this paper assesses the methodological developments, including fuzzy-enhanced weighting, the use of entropy-based extensions, CRITIC variants, Bayesian and probabilistic schemes, combined Delphi-ANP-DEMATEL approaches, best-worst method (BWM) refinements, and new deep-learning-based weight estimators. The review points out the ability of such modern approaches to boost robustness, decrease subjectivity, and increase transparency in decision-making in a variety of MCDM models. It also establishes the gaps in methodology, current trends, and future research directions and sets new weighting strategies as a key tool in managing complex, uncertain, and high-dimensional decision problems. This combination helps them gain a better theoretical insight as well as provide some practical advice to researchers and practitioners wishing to apply advanced weighting methods in MCDM.

Downloads

Download data is not yet available.

References

Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25-48. https://doi.org/10.31181/dma1120237

Więckowski, J., Sałabun, W., Kizielewicz, B., Bączkiewicz, A., Shekhovtsov, A., Paradowski, B., & Wątróbski, J. (2023). Recent advances in multi-criteria decision analysis: A comprehensive review of applications and trends. International Journal of Knowledge-based and Intelligent Engineering Systems, 27(4), 367-393. https://doi.org/10.3233/KES-230487

Kumar, R., & Pamucar, D. (2025). A comprehensive and systematic review of multi-criteria decision-making (MCDM) methods to solve decision-making problems: two decades from 2004 to 2024. Spectrum of Decision Making and Applications, 2(1), 178-197. https://doi.org/10.31181/sdmap21202524

Demir, G., Chatterjee, P., & Pamucar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660

Moktadir, M. A., Paul, S. K., Bai, C., & Santibanez Gonzalez, E. D. (2025). The current and future states of MCDM methods in sustainable supply chain risk assessment. Environment, Development and Sustainability, 27(3), 7435-7480. https://doi.org/10.1007/s10668-023-04200-1

Więckowski, J., Hernes, M., & Sałabun, W. (2025). Comparison of Multi-Criteria Decision Analysis methods under comprehensive sensitivity analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3588166

Sahoo, S. K., Pamucar, D., & Goswami, S. S. (2025). A review of multi-criteria decision-making (MCDM) applications to solve energy management problems from 2010-2025: Current state and future research. Spectrum of Decision Making and Applications, 2(1), 219-241. https://doi.org/10.31181/sdmap21202525

Ismail, M. M., Abdelhady, H. R., & Emad, M. (2024). Multi-criteria decision-making techniques: a comprehensive review of methodologies and applications. International Journal of Computers and Informatics (Zagazig University), 2, 27-38. https://www.ijci.zu.edu.eg/index.php/ijci/article/view/70

Khan, N. A., Kumar, A., & Rao, N. (2025). An Insight into Multi-Criteria Decision Methods for the Selection of Robot: A Comprehensive Review. SN Computer Science, 6(6), 612. https://doi.org/10.1007/s42979-025-04143-6

Rishabh, R., & Das, K. N. (2025). A critical review on metaheuristic algorithms based multi-criteria decision-making approaches and applications. Archives of Computational Methods in Engineering, 32(2), 963-993. https://doi.org/10.1007/s11831-024-10165-9

Avramova, T., Peneva, T., & Ivanov, A. (2025). Overview of Existing Multi-Criteria Decision-Making (MCDM) Methods Used in Industrial Environments. Technologies, 13(10), 444. https://doi.org/10.3390/technologies13100444

Singh, R., Pathak, V. K., Kumar, R., Dikshit, M., Aherwar, A., Singh, V., & Singh, T. (2024). A historical review and analysis on MOORA and its fuzzy extensions for different applications. Heliyon, 10(3). https://doi.org/10.1016/j.heliyon.2024.e25453

Gyani, J., Ahmed, A., & Haq, M. A. (2022). MCDM and various prioritization methods in AHP for CSS: A comprehensive review. IEEE Access, 10, 33492-33511. https://doi.org/10.1109/ACCESS.2022.3161742

Singh, S. P., Mehta, A., & Vasudev, H. (2025). Application of sensitivity analysis for multiple attribute decision making in lean production system. Engineering Management Journal, 1-24. https://doi.org/10.1080/10429247.2024.2383855

Ouhmida, S., Moulay Abdelali, H., & Lamdouar, N. (2025). Revolutionizing bridge rehabilitation through artificial intelligence: a comprehensive review and future directions. Asian Journal of Civil Engineering, 1-15. https://doi.org/10.1007/s42107-025-01322-x

Murugan, R. S., & Vinodh, S. (2025). Holistic review on design for additive manufacturing. Progress in Additive Manufacturing, 10(8), 4497-4532. https://doi.org/10.1007/s40964-024-00887-4

Doost, Z. H., Alsuwaiyan, M., & Yaseen, Z. M. (2024). Runoff management based water harvesting for better water resources sustainability: a comprehensive review. Knowledge-Based Engineering and Sciences, 5(1), 1-45. https://doi.org/10.51526/kbes.2024.5.1.1-45

Ray, S. K. (2025). Integrating Geographic Information System, Artificial Intelligence, and Multi-Criteria Decision Analysis: A Comprehensive Review for Sustainable Urban Settlement Planning. Applied Spatial Analysis and Policy, 18(4), 155. https://doi.org/10.1007/s12061-025-09762-3

Gacu, J. G., Monjardin, C. E. F., Mangulabnan, R. G. T., Pugat, G. C. E., & Solmerin, J. G. (2025). Artificial Intelligence (AI) in Surface Water Management: A Comprehensive Review of Methods, Applications, and Challenges. Water, 17(11), 1707. https://doi.org/10.3390/w17111707

Abirami, K. M., Veena, N., Srikanth, R., & Dhanasekaran, P. (2024). An extensive review of the literature using the Diophantine equations to study fuzzy set theory. International Journal of Mathematics and Mathematical Sciences, 2024(1), 5014170. https://doi.org/10.1155/2024/5014170

Karatzinis, G. D., & Boutalis, Y. S. (2025). A review study of fuzzy cognitive maps in engineering: applications, insights, and future directions. Eng, 6(2), 37. https://doi.org/10.3390/eng6020037

Cao, Y., Cui, J., Liu, S., Li, X., Zhou, Q., Hu, C., ... & Liu, Z. (2023). A holistic review on e-mobility service optimization: Challenges, recent progress, and future directions. IEEE Transactions on Transportation Electrification, 10(2), 3712-3741. https://doi.org/10.1109/TTE.2023.3311410

El Madou, K., Marso, S., El Kharrim, M., & El Merouani, M. (2024). Evolutions in machine learning technology for financial distress prediction: A comprehensive review and comparative analysis. Expert Systems, 41(2), e13485. https://doi.org/10.1111/exsy.13485

Więckowski, J., & Sałabun, W. (2025). Comparative sensitivity analysis in composite material selection: Evaluating OAT and COMSAM methods in multi-criteria decision-making. Spectrum of Mechanical Engineering and Operational Research, 2(1), 1-12. https://doi.org/10.31181/smeor21202524

Bilquise, G., Shaalan, K., & AlKhatib, M. (2025). A Comprehensive Review of Virtual Commerce Applications for the Metaverse: Open Issues, Challenges and Recommended Solution for Benchmarking. International Journal of Human–Computer Interaction, 1-27. https://doi.org/10.1080/10447318.2025.2499662

Kuhaneswaran, B., Chamanee, G., & Kumara, B. T. G. S. (2025). A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective. Waste Management & Research, 43(2), 137-159. https://doi.org/10.1177/0734242X241237100

Losada-Agudelo, M., & Souyris, S. (2024). Sustainable operations management in the energy sector: A comprehensive review of the literature from 2000 to 2024. Sustainability, 16(18), 7999. https://doi.org/10.3390/su16187999

Yu, S., & Mu, Y. (2022). Sustainable agricultural development assessment: A comprehensive review and bibliometric analysis. Sustainability, 14(19), 11824. https://doi.org/10.3390/su141911824

Adem, A., Çakit, E., Dağdeviren, M., Szopa, A., & Karwowski, W. (2025). A Symbiosis of Multi-Criteria Decision Making and Electroencephalography: A Review of Techniques, Applications, and Future Directions. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3562099

Anbari Moghadam, M., & Besiktepe, D. (2025). Synthesis of multi-criteria decision-making applications in facilities management and building maintenance: Trends, methods, and future research directions. Buildings, 15(18), 3258. https://doi.org/10.3390/buildings15183258

Mushthofa, M., Thedy, J., Teguh, M., Purwanto, Pratama, A. G., & Han, A. L. (2025). Artificial Intelligence in Geopolymer Concrete Mix Design: A Comprehensive Review of Techniques and Applications. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-33. https://doi.org/10.1007/s40996-025-01873-8

Abdel-Basset, M., Mohamed, R., & Chang, V. (2025). A multi-criteria decision-making framework to evaluate the impact of industry 5.0 technologies: case study, lessons learned, challenges and future directions. Information Systems Frontiers, 27(2), 791-821. https://doi.org/10.1007/s10796-024-10472-3

Lu, Z., Liu, G., Song, Z., Sun, K., Li, M., Chen, Y., Zhao, X., & Zhang, W. (2024). Advancements in technologies and methodologies of machine learning in landslide susceptibility research: current trends and future directions. Applied Sciences, 14(21), 9639. https://doi.org/10.3390/app14219639

Kut, P., & Pietrucha-Urbanik, K. (2024). Bibliometric Analysis of Multi-Criteria Decision-Making (MCDM) Methods in Environmental and Energy Engineering Using CiteSpace Software: Identification of Key Research Trends and Patterns of International Cooperation. Energies, 17(16), 3941. https://doi.org/10.3390/en17163941

Shah, H. M., Gardas, B. B., Narwane, V. S., & Mehta, H. S. (2023). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes, 52(5), 1643-1697. https://doi.org/10.1108/K-05-2021-0423

Viriyasitavat, W., Da Xu, L., Niyato, D., Bi, Z., & Hoonsopon, D. (2022). Applications of blockchain in business processes: A comprehensive review. IEEE Access, 10, 118900-118925. https://doi.org/10.1109/ACCESS.2022.3217794

Tesic, D., Pamučar, D., Demir, G., Puška, A., & Božanić, D. (2026). Aggregation of Expert Ranking Opinions Using Multi-Criteria Decision-Making Methods. Spectrum of Operational Research, 1-14. https://doi.org/10.31181/sor202780