Using MOLP based procedures to solve DEA problems

Data envelopment analysis (DEA) is a technique used to evaluate the relative efficiency of comparable decision making units (DMUs) with multiple input-output. It computes a scalar measure of efficiency and discriminates between efficient and inefficient DMUs. It can also provide reference units for inefficient DMUs without consideration of the decision makers’ (DMs) preferences. In this paper, we deal with the problem of incorporating preferences over potential improvements to individual input output levels so that the resultant target levels reflect the DM’s preferences over alternative paths to efficiency. In this way, the paper will establish an equivalence model between DEA and multiple objective linear programming (MOLP) and show how a DEA problem can be solved interactively by transforming it into an MOLP formulation. As a result, all efficient units of variable returns to scale technology in DEA can be found by solving the proposed MOLP problem by parametric linear programming. Numerical examples confirm the validity of the proposed model as a means for solving different DEA problems.