Benchmarking and Data Envelopment Analysis. An Approach based on Metaheuristics

Data Envelopment Analysis (DEA) is a non-parametric technique for estimating technical efficiency of a set of units. DEA also provides information on benchmarking. In this paper, we study DEA models based on closest efficient targets, which are associated with the least distance and allow inefficient units to find the easiest way to achieve the efficient frontier. In the literature these models have been solved through unsatisfactory methods related to combinatorial NP-hard problems. In this paper, the problem is approached by metaheuristic techniques. Due to the high number of restrictions of the problem, finding solutions to be used in the metaheuristic algorithm is a difficult problem. Thus, this paper analyzes and compares some heuristic algorithms to obtain solutions of the problem. Each restriction determines the design of these heuristics. Thus, the problem is considered by adding constraints one by one. In this paper, the problem is presented and studied taking into account 9 of the 14 constraints, and the solution to this new problem is an upper bound of the optimal value of the original problem.