A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems

This study presents a novel Full Multiplicative Data Envelopment Analysis (FMDEA) for solving Multi-Attribute Decision-Making (MADM) problems. The proposed model offers an innovative approach to solving MADM problems by integrating the principles of Data Envelopment Analysis (DEA) with Full Multiplicative Form (FMF). This approach effectively addresses the significant limitations of traditional MADM methods, particularly concerning data normalization and computational complexity. We demonstrate the robustness and reliability of the proposed FMDEA model through its application across various decision-making scenarios. We demonstrate the perfect alignment of FMDEA with various decision-making scenarios, such as Multi-Objective Optimization with Full Multiplicative Form (MOOFMF), Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Aggregated Sum Product Assessment (WASPAS), and Complex Proportional Assessment (COPRAS) in flexible manufacturing. The model exhibited high correlations with MOORA, MOOFMF, TOPSIS, WASPAS, and COPRAS. The FMDEA model consistently aligned with MOORA, MOOFMF, TOPSIS, WASPAS, and COPRAS in Computer Numerical Control (CNC) lathe selection. These results confirm the FMDEA model’s effectiveness in addressing MADM challenges by offering a simple, versatile, and user-friendly framework compatible with various optimization solvers, thus enhancing its practical applicability in complex decision-making contexts. • Propose a full multiplicative data envelopment analysis to enhance multi-attribute decision-making. • Show the proposed model addresses data normalization and computational complexity limitations in multi-attribute decision-making. • Simplify complex multi-attribute decision-making by automating normalization and efficiently handling high-dimensional data. • Demonstrate the proposed model has a high Spearman correlation coefficient with other multi-attribute decision-making methods.