An Improved Common Weight DEA-Based Decision Approach for Problems with Multiple Inputs and Multiple Outputs

摘要

This paper proposes a common-weight data envelopment analysis (DEA) based model for identifying the best performing decision making unit (DMU) considering multiple inputs and multiple outputs. In order to illustrate the robustness of the developed model, which provides better weight dispersion and an improved discriminating power in ranking DMUs, a comparative analysis of the results of the numerical examples addressed in an earlier study are given. The results demonstrate that the proposed approach yields better dispersion for input and output weights while it docs not require an arbitrary discriminating parameter and guarantees to determine the most efficient DMU via solving a single mixed integer linear programming model.