Integrated data envelopment analysis, multi-criteria decision making, and cluster analysis methods : trends and perspectives

Data Envelopment Analysis (DEA), Multi-criteria Decision Analysis (MCDA), and Cluster Analysis (CA) are techniques widely used to help decision-makers determine the solution to problems with multiple and often conflicting criteria. This study presents a comprehensive review of the literature on the DEA, MCDA, and CA models to identify existing and potential applications and future development trends. To this end, the Methodi Ordinatio was applied to determine which publications have the greatest impact, considering articles in three databases: Scopus, ScienceDirect, and Web of Science. We pair the techniques two by two since no results were found to integrate the three techniques. The results point to a portfolio with 490 articles, in which approximately 43.87% of the articles combine DEA and MCDA techniques for solving efficiency and productivity analysis problems.