The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered wit...

in a recent paper in this journal, yang et al. [feng yang, dexiang wu,liang liang, gongbing bi & desheng dash wu (2009), supply chaindea:production possibility set and performance evaluation model] definedtwo types of supply chain production possibility set which were proved to beequivalent to each other. they also proposed a new model for evaluatingsupply chains. there are, however, some short...

Despite the large uses of inverse DEA models, there is not any single application of inverse linear programming in DEA when the definition of inverse linear programming is taken under account. Thus the goal of this paper is applying the inverse linear programming into DEA field, and to provide a streamlined approach to DEA and Additive model. Having the entire efficient DMUs in DEA models is a...

data envelopment analysis (dea) is a method for measuring the efficiency of peer decision making units (dmus) which uses a set of inputs to produce a set of outputs. in some cases, dmus have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. one important issue in two-stage dea is the sensitivity of...

Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the r...

this paper proposes an alternative approach for efficiency analysis when a set of dmus uses interval scale variables in the productive process. to test the influence of these variables, we present a general approach of deriving dea models to deal with the variables. we investigate a number of performance measures with unrestricted-in-sign interval and/or interval scale variables.

This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 19...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (DMUs) with multiple inputs and multiple outputs. The original DEA models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...

Interval data envelopment analysis (interval DEA) deals with the problem of efficiency assessment when inputs and/or outputs decision making units (DMUs) are given as interval data. This paper focuses on ranking DMUs First, we define extreme efficient units, super score, best and worst (inefficiency) frontiers in DEA context. Then, propose a novel method based lower upper scores unit under eval...

In this paper a model for fuzzy SBM of efficiency in DEA is presented based on a-cuts of the fuzzy number parameters to convert fuzzy linear programming problem to an interval programming problem. Then, by introducing a variable in each of the obtained intervals and applying suitable substitutions, a linear programming problem would be achieved. To demonstrate the concept, numerical examples ar...