Data envelopment analysis: an efficient duo linear programming approach

Data envelopment analysis (DEA) is a powerful mathematical method that utilises linear programming (LP) to determine the relative efficiencies of a set of functionally similar decision-making units (DMUs). Evaluating the efficiency of DMUs continues to be a difficult problem to solve, especially when the multiplicity of inputs and outputs associated with these units is considered. Problems related to computational complexities arise when there are a relatively large number of redundant variables and constraints in the problem. In this paper, we propose a three-step algorithm to reduce the computational complexities and costs in the multiplier DEA problems. In the first step, we identify some of the inefficient DMUs through input–output comparisons. In the second step, we specify the efficient DMUs by solving a LP model. In the third step, we use the results derived from the second step and another LP model to obtain the efficiency of the inefficient DMUs. We also present a numerical example to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.