Mapping the Evolution of Multi-Criteria Decision-Making and Simple Additive Weighting Research: A Comprehensive Bibliometric and Science Mapping Analysis from 2000 to 2025 - Alfry Aristo Jansen Sinlae; Zulfikar Zulfikar; Juni Ismail; Yanto Saputra; Raja Anan Nasution; Elsy Rahajeng; Mesran Mesran (Bulletin of Artificial Intelligence, 2025)
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