جستجوی مقالات مرتبط با کلیدواژه « Flexible measures » 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.

In this paper we propose FNDEA models based on the multiplier model under constant returns to scale (CRS) and variable returns to scale (VRS) in general two-stage network structures to classify flexible measures, in which each one of the flexible measure is treated as either input or output to maximize the overall network efficiency of the DMU under evaluation.

In Data Envelopment Analysis (DEA), it is assumed that the role of each factor is known as input or output. However, in some cases, there are shared factors that their input versus output status is not clearly known. These are flexible measures. In such cases, determining whether a factor is input or output is ambiguous. Therefore, using fuzzy concept seems to be necessary. In this paper, a two phase procedure is proposed to fuzzy classification of flexible measures. In the first phase, applying the existing classification methods, an orientation of flexible measures to aid in the definition of inputs and outputs is achieved. Through defining a membership function in second phase, the input versus output status of a factor is expressed by fuzzy notion. By the proposed method, the efficiency of a decision mating unit is defended by a membership degree. We illustrate the proposed model in a practical problem setting.

Data envelopment analysis (DEA) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (DMUs) where multiple inputs and outputs exist. In the conventional applications of DEA, the data are considered as specific numerical values with explicit designation of being an input or output. However, the observed values of the data are sometimes imprecise (i.e. input and output variables cannot be measured precisely) and data are sometimes flexible (measures with unknown status of being input or output are referred to as flexible measures in the literature). In the current paper a number of methods are proposed to evaluate the relative efficiency and to identify the status of fuzzy flexible measures. Indeed, the modified fuzzy DEA models are suggested to accommodate flexible measures. In order to obtain correct results, alternative optimal solutions are considered to deal with the fuzzy flexible measures. Numerical examples are used to illustrate the procedure.