DEA Comprehensive Evaluation Method Implemented in MATLAB

The MATLAB-implemented DEA comprehensive evaluation method provides a robust framework for assessing the relative efficiency of multiple decision-making units (DMUs). This implementation typically utilizes linear programming techniques to construct efficient frontiers and calculate efficiency scores based on multiple input and output variables. Key implementation aspects include: - Data envelopment analysis (DEA) algorithm implementation using MATLAB's optimization toolbox - Handling of both CCR (Charnes-Cooper-Rhodes) and BCC (Banker-Charnes-Cooper) models - Efficiency score calculation through linear programming formulations - Visualization of efficient frontiers and peer comparisons The method enables comprehensive performance evaluation across various domains, from corporate performance assessment to public service efficiency analysis. Its algorithmic flexibility allows adaptation to different scale returns assumptions and orientation preferences (input-minimization vs output-maximization). Decision-makers can leverage this implementation to identify benchmarking targets, optimize resource allocation, and pinpoint improvement areas through slack variable analysis. The MATLAB code typically includes functions for data normalization, efficiency frontier construction, and results interpretation, providing actionable insights for operational enhancements and strategic decision-making.