In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct...
In the performance measurement using tools such as data envelopment analysis (DEA), data without explicit inputs has attracted considerable attention among researchers. In such studies the problem of production planning in the next production season is an important and interesting subject. Because of the uncertain nature of the future, decision makers need to provide robust procedures in order to examine alternative courses of action and their implications. The purpose of this paper is to develop...
Data Envelopment Analysis (DEA) is a beneficial mathematical programming method to measure relative efficiencies. In conventional DEA models, Decision Making Units (DMUs) are usually considered as black boxes. Also, the efficiency of DMUs is evaluated in the presence of the specified inputs and outputs. Nevertheless, in real-world applications, there are situations in which the performance of multi-stage processes like supply chains with forward and reverse flows must be measured such that some...
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