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 each parallel network system improve or p...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Crisp input and output data are fundamentally indispensable in traditional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or ambiguous. In this study, we present a four-p...

DEA models treat the DMU as a “black box.” Inputs enter and outputs exit, with no consideration of the intervening steps. Consequently, it is di(cult, if not impossible, to provide individual DMU managers with speci;c information regarding the sources of ine(ciency within their DMUs. We show how to use DEA to look inside the DMU, allowing greater insight as to the sources of organizational ine(...

Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficiency scores of decision making units (DMUs). Since DEA models cannot present efficient frontiers of PPS, in order to do this, we introduce a method for identifying efficient frontier for DMUs with interval data.

Data envelopment analysis (DEA) is a powerful mathematical method that utilises linear programming (LP) to determine the relative efficiencies of a set of functionally similar decision-making units (DMUs). Evaluating the efficiency of DMUs continues to be a difficult problem to solve, especially when the multiplicity of inputs and outputs associated with these units is considered. Problems rela...

The cross-efficiency method, as a Data Envelopment Analysis (DEA) extension, calculates the cross efficiency of each decision making unit (DMU) using the weights of all decision making units (DMUs). The major advantage of the cross-efficiency method is that it can provide a complete ranking for all DMUs. In addition, the cross-efficiency method could eliminate unrealistic weight results. Howeve...

How to evaluate the performance of decision-making units (DMUs) with fixed-sum outputs is a timely and challenging question in data envelopment analysis (DEA). Two major challenges are (1) how determine common equilibrium efficient frontier (2) deal multiple feasible frontiers. This paper first uses simple dataset illustrate possibility frontiers corresponding differences DMUs’ efficiencies ran...

Sensitivity analysis in DEA is used for improving the efficiency scores of inefficient DMUs for which the efficient units remain unchanged. This paper introduces a generalized sensitivity analysis DEA model by perturbation a given input (or output) for all efficient DMUs. A numerical example illustrates the usefulness of the new model.