Several researchers have considered similarities between Multi-Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA), as tools for solving decision making problems. As the preferences of decision- maker (DM) on alternatives are not considered in classical DEA, some researchers have tried to consider it in DEA. The UTA-STAR method is one of the techniques widely used in Multi Criteria Decision Analysis. In this technique, the preferences of decision maker on alternatives are considered and UTA-STAR tries to compute the most suitable weights for criteria and alternatives to obtain a utility function having a minimum deviation from the preferences. The goal of this paper is interpreting decision maker’s preferences in UTA-STAR method, in a new manner, using the common set of weights (CSW) in DEA.

This article introduces a scientometric investigation regarding the use of Multi-Criteria Decision-Making (MCDM) techniques in the area of portfolio optimization. The study, which employs a carefully selected dataset of 108 scholarly articles that are drawn from the Scopus database, covers the period 2003 to 2026, and maps the intellectual landscape, identifies the leading methodologies, and tracks the trends of migration. We perform a systematic analysis of the occurrence, impact, and application domains of 20 different MCDM methods, which include Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), TOPSIS and PROMETHEE, among others. The study employs key indicators such as the number of publications, the number of citations, the geographical distribution of authors, as well as the dispersion of journals, to assess the impact and uptake of the various techniques. The analysis demonstrates the dominance of DEA as a method, which is often combined with other MCDM methods and metaheuristics. A trend towards hybridization, which includes the combination of MCDM with fuzzy set theory, machine learning, and evolutionary algorithms, has been recognized as one of the main factors contributing to the innovation of recent techniques. In addition, the study points out the increasing adoption of Environmental, Social, and Governance (ESG) factors and big data analytics into the portfolio selection process. The survey presents a quantitative picture of the domain and provides researchers and practitioners with valuable insights through the identification of the established pillars, the emerging hotspots, and the future research trajectories in the field of MCDM-based portfolio optimization.