جستجوی مقالات مرتبط با کلیدواژه « Classi cation » در نشریات گروه « ریاضی »

Data envelopment analysis (DEA) obtains the relative eciency of decision-making units (DMUs) based on their inputs and outputs. In many situations, some variables can play the role of input or output for a DMU. It is important to provide a suitable model that can maximize the eciency of the unit under evaluation by correctly choosing the role of these variables. One of the production technologies in DEA is semi-additive production technology. In addition to the observed DMUs, this technology also considers the set of all aggregations corresponding to these DMUs in the evaluation of eciency. In this paper, we presented the semi-additive production technology in DEA in the presence of exible measures. This technology is created based on the observed DMUs and their corresponding aggregations DMUs. We have shown that we can provide semi-additive production technology in the presence of exible measures based only on the observed DMUs by removing the region with decreasing returns to scale from the production possibility set (PPS). In the following, we present two dierent approaches for measuring the eciency of DMUs in the presence of exible measures in semi-additive production technology. These two approaches allocate exible measure as input or output in such a way that the eciency of the unit under evaluation is maximized. Also, we use the proposed approach to evaluate the eciency of data sets related to academic units.