The Measuring Efficiency of Large Scale Datasets in DEA with Metaheuristic Algorithm Approach

Data Envelopment Analysis (DEA) is a non-parametric technique for measuring the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. DEA for a large dataset with many input/output variables and/or many DMUs would need huge computer resources in terms of memory and CPU time. This paper proposed an Electromagnetism Algorithm (EA) for estimating the efficiency of DMUs in large datasets for the first time. Since the parameters have important roles on the convergence and quality of the algorithms, they are calibrated by means of the experimental design in order to improve their performances. To evaluate the effectiveness of EM, a numerical experiment was conducted using several data sets and compared with simulated annealing (SA) Algorithm as a well-known metaheuristic. Experimental results indicated that EM outperformed SA.