Mapping the Evolution of Multi-Criteria Decision-Making and Simple Additive Weighting Research: A Comprehensive Bibliometric and Science Mapping Analysis from 2000 to 2025
Abstract
The increasing complexity of decision-making environments driven by digital transformation, sustainability challenges, and technological advancements has significantly accelerated the adoption of Multi-Criteria Decision-Making (MCDM) methods across diverse scientific and practical domains. Among these approaches, the Simple Additive Weighting (SAW) method has gained substantial attention due to its simplicity, transparency, and effectiveness in evaluating alternatives based on multiple criteria. Despite the rapid growth of MCDM-SAW studies, the existing body of knowledge remains fragmented across disciplines, institutions, and application areas, creating a need for a comprehensive assessment of its intellectual and thematic development. Therefore, this study aims to systematically map the evolution, intellectual structure, and emerging research trends of MCDM and SAW research from 2000 to 2025. A bibliometric research design combined with science mapping techniques was employed using data retrieved from the Scopus database. A total of 381 English-language publications were selected through a PRISMA-based screening process. Data analysis was conducted using the Scopus Analysis Tool for performance analysis and VOSviewer for network visualization, including publication trend analysis, subject area distribution, co-citation analysis, and keyword co-occurrence mapping. The findings reveal a substantial increase in scientific production, particularly after 2015, indicating the growing relevance of MCDM and SAW in contemporary decision-support research. Engineering and Computer Science emerged as the most dominant subject areas, while leading publication sources included Expert Systems with Applications, Mathematics, Sustainability, and IEEE Access. Co-citation analysis identified influential scholars and foundational theories that shape the field, whereas keyword co-occurrence analysis highlighted the growing integration of sustainability, optimization, artificial intelligence, and hybrid MCDM frameworks. The novelty of this study lies in its integrated examination of publication performance, intellectual structure, and thematic evolution within the MCDM-SAW domain. The study contributes by providing a comprehensive knowledge map that supports future theoretical development, interdisciplinary collaboration, and methodological innovation in decision-support research
References
Y. Li, X. He, L. Martínez, J. Zhang, D. Wang, and X. A. Liu, “Comparative analysis of three categories of multi-criteria decision-making methods,” Expert Syst. Appl., vol. 238, p. 121824, Mar. 2024, doi: 10.1016/j.eswa.2023.121824.
P. Villalba, A. J. Sánchez-Garrido, and V. Yepes, “A review of multi-criteria decision-making methods for building assessment, selection, and retrofit,” J. Civ. Eng. Manag., vol. 30, no. 5, pp. 465–480, Jun. 2024, doi: 10.3846/jcem.2024.21621.
Z. N. Shehab, R. M. Faisal, and S. W. Ahmed, “Multi-criteria decision making (MCDM) approach for identifying optimal solar farm locations: A multi-technique comparative analysis,” Renew. Energy, vol. 237, p. 121787, Dec. 2024, doi: 10.1016/j.renene.2024.121787.
P. Kumar et al., “Optimizing Electric Mobility: A Multi-Criteria Decision-Making Approach for Sustainable Future of Electric Vehicles Through Smart Motor Choices,” J. Eur. Systèmes Autom., vol. 57, no. 6, pp. 1825–1845, Dec. 2024, doi: 10.18280/jesa.570630.
Ž. Stević, N. Ersoy, E. E. Başar, and M. Baydaş, “Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework,” Appl. Sci., vol. 14, no. 22, p. 10334, Jan. 2024, doi: 10.3390/app142210334.
A. Al Masri, A. N. Haddad, and M. K. Najjar, “Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making,” Computation, vol. 12, no. 12, p. 247, Dec. 2024, doi: 10.3390/computation12120247.
K. Nabiollahi et al., “Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation,” Remote Sens., vol. 16, no. 14, p. 2566, Jan. 2024, doi: 10.3390/rs16142566.
D. V. Thanh, V. D. Binh, V. Duong, L. A. Tung, and T. Q. Hung, “Application of SAW Technique for Finding the Best Dressing Mode for Surface Grinding Hardox 500,” Int. J. Mech., vol. 18, pp. 17–20, Jun. 2024, doi: 10.46300/9104.2024.18.3.
V.-T. Dinh, H.-D. Tran, D.-B. Vu, D. Vu, N.-P. Vu, and A.-T. Luu, “Application of a Multi-Criterion Decision-Making Method for Solving the Multi-Objective Optimization of a Two-Stage Helical Gearbox,” Machines, vol. 12, no. 6, p. 365, Jun. 2024, doi: 10.3390/machines12060365.
Sumanto et al., “Improved LOPCOW-SAW Method for Optimal Supplier Selection in Supply Chain Management,” in 2024 12th International Conference on Cyber and IT Service Management (CITSM), Oct. 2024, pp. 1–5. doi: 10.1109/CITSM64103.2024.10775429.
T. Porchudar, A. M. Anita, M. A. J. Shalini, and J. J. Jesintha, “An MCDM Based on Neutrosophic Fuzzy SAW Method for New Entrepreneurs in Organic Farming,” in Recent Developments in Algebra and Analysis, H.-H. Leung, R. Sivaraj, and F. Kamalov, Eds., Cham: Springer International Publishing, 2024, pp. 67–75. doi: 10.1007/978-3-031-37538-5_7.
T. V. Dua, “PSI-SAW and PSI-MARCOS Hybrid MCDM Methods,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 4, pp. 15963–15968, Aug. 2024, doi: 10.48084/etasr.7992.
S. Khodabakhshi, M. A. A. Habib, and W. Peng, “A Decision-Making System for Medical Transportation Mode Using Machine Learning Methods,” Eng. Proc., vol. 76, no. 1, p. 74, 2024, doi: 10.3390/engproc2024076074.
V. Grybaitė and A. Burinskienė, “Assessment of Circular Economy Development in the EU Countries Based on SAW Method,” Sustainability, vol. 16, no. 21, p. 9582, Jan. 2024, doi: 10.3390/su16219582.
N. Ersoy and N. Keleş, “Comparison of multi-criteria decision-making methods with the same normalization procedure based on real-life applications,” Oper. Res. Decis., vol. 34, no. 3, 2024, doi: 10.37190/ord240305.
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