Metaheuristics for data envelopment analysis problems

Abstract

In conventional data envelopment analysis (DEA) models, some inputs and outputs may lose their weights in the process of measuring the relative efficiency of DMUs and it leads to ignore corresponding inputs and outputs. Also, using the DEA for a large dataset needs to have a long time with a high-speed computer. In order to solve the problem, genetic algorithm (GA) and differential evolution (DE) algorithm are adopted and proposed in this paper. The GA is used in previous papers but here some operators are used for the first time. Besides, DE algorithm is adopted and used to solve the problem via new operators which are first introduced in this paper. Moreover, a plan is utilised to generate test problems in different sizes in this work. Since the crossover and mutation operators have significant impacts on the algorithms’ performance, all the operators and parameters are calibrated by means of the Taguchi experimental design in order to improve their performances. The performances of the proposed algorithms are then evaluated by comparing their solutions based on the presented instances. Finally, the impacts of increasing the problem size on the performance of our proposed algorithms are investigated.