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Advances in Operations Research
Volume 2014 (2014), Article ID 431749, 9 pages
Research Article

Benchmarking in Data Envelopment Analysis: An Approach Based on Genetic Algorithms and Parallel Programming

Center of Operations Research (CIO), University Miguel Hernandez of Elche, Avenida de la Universidad s/n, 03202 Elche (Alicante), Spain

Received 20 October 2013; Accepted 28 December 2013; Published 13 February 2014

Academic Editor: Shangyao Yan

Copyright © 2014 Juan Aparicio et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Data Envelopment Analysis (DEA) is a nonparametric technique to estimate the current level of efficiency of a set of entities. DEA also provides information on how to remove inefficiency through the determination of benchmarking information. This paper is devoted to study DEA models based on closest efficient targets, which are related to the shortest projection to the production frontier and allow inefficient firms to find the easiest way to improve their performance. Usually, these models have been solved by means of unsatisfactory methods since all of them are related in some sense to a combinatorial NP-hard problem. In this paper, the problem is approached by genetic algorithms and parallel programming. In addition, to produce reasonable solutions, a particular metaheuristic is proposed and checked through some numerical instances.