A new approach on the lowest cost problem in data envelopment analysis. (20th October 2021) - Wang, Xu; Lu, Kuan; Hasuike, Takashi
This paper aims at solving the lowest cost problem in data envelopment analysis (DEA), which is to provide an efficient target for an inefficient decision making unit (DMU) with the lowest adjustment costs. For this purpose, a new approach based on the least distance DEA model is proposed. Here, the marginal costs of adjusting the inputs and outputs are assumed to be known and symmetrical. For the practical merit, different with the existing studies, our approach is able to increase inputs and decrease outputs. Numerical experiments are conducted to compare the performance of the proposed approach with previous existing studies. The results show that the proposed approach can always provide an efficient target with no higher total adjustment costs than the costs of targets provided by previous approaches. Thus, the proposed approach is more practical and useful.