Zahra Ghelej Beigi

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Data envelopment analysis (DEA) is a non-parametric approach to evaluate a set of decision making units (DMUs) consuming multiple inputs to produce multiple outputs. Formally, DEA use to estimate the efficiency score into the empirical efficient frontier. Also, DEA can be used to allocate resources and set targets for future forecast. The data are(More)
Data Envelopment Analysis (DEA) is a powerful tool for measuring the relative efficiency for a set of Decision Making Units (DMUs) that transform multiple inputs into multiple outputs. In centralized decisionmaking systems, management normally imposes common resource constraints to maximize operating revenues and minimize operating expenses. In this study,(More)
Purpose – The purpose of this paper is to consider the following problem; if the manager of the parallel network systems wants to add new sub-decision making units (sub-DMUs) to each parallel network system, he/she wants to know how much new fuzzy inputs allocate to new sub-DMUs and how much outputs these new sub-DMUs produce such that the efficiency of(More)
Data envelopment analysis (DEA) is a powerful tool for measuring the relative efficiencies of a set of decision making units (DMUs) such as schools and bank branches that transform multiple inputs to multiple outputs. In centralized decision-making systems, management normally imposes common resource constraints such as fixed capital, budgets for operating(More)
Data envelopment analysis (DEA) is amathematical programmingapproach for evaluating the relative efficiency of decision making units (DMUs) in organizations. The conventionalDEAmethods require accuratemeasurement of both the inputs and outputs. However, the observed values of the input and output data in real-world problems are often imprecise or vague.(More)
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