inthis article we offer a method of ranking contractors by using dea based onanalysis deficit and ahp. the process of hierarchical analysis (ahp) byproviding scales from paired comparison matrix, performs the contractor’sprioritizing choice. but ahp has some problems and to solve those problems,jahanshahloo and his colleagues presented a new model which uses dea andstandard deviation. in this a...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...

UT HO R CO PY Two-stage network DEA: when intermediate measures can be treated as outputs from the second stage Sonia Aviles-Sacoto, Wade D Cook*, Raha Imanirad and Joe Zhu Doctoral Program in Engineering Sciences, ITESM-Monterrey, Mexico; Schulich School of Buisness, York University, Toronto, Canada M3J1P3; Doctoral Program, Harvard Business School, Soldiers Field, Boston, MA 02163; and Intern...

The purpose of this study is to present a complementary modeling approach using data envelopment analysis (DEA) and artificial neural network (ANN) as an adaptive decision support tool in promoting best performance benchmarking and performance modeling. DEA and ANN are combined to take advantages of optimization and prediction capabilities inherent in each method. DEA is used as a preprocessor ...

Data envelopment analysis (DEA), based on mathematical programming techniques, is a popular method for assessing the relative efficiency of various decision-making units (DMUs). Efficient DEA computations need to circumvent intensive time-consuming calculations. This paper indicates that the “corner points” in single input and single output scatter plot has extreme efficiency with regard to the...

Dissociative electron attachment (DEA) is the major process where molecules are destroyed in low-energy plasmas. DEA cross sections are therefore important for a whole variety of applications but are both hard to measure or compute accurately. A method for estimating DEA cross sections based a simple resonance plus survival model is presented. Test results are presented for DEA of molecular oxy...

Dyson et al. [Dyson, R.G., Allen, R., Camanho, A.S., Podinovski, V.V., Sarrico, C.S., Shale, E.A., 2001. Pitfalls and protocols in DEA. European Journal of Operational Research 132, 245–259] suggest that the application of DEA presents a range of problems and they suggest that the systematic collection and reporting of these problems would significantly enhance DEA practice. It is widely accept...

Traditional data envelopment analysis (DEA) models deal with measurement of relative efficiency of decision making units (DMUs) in which multiple-inputs consumed to produce multiple-outputs. One of the drawbacks of these models is neglecting internal processes of each system, which may have intermediate products and/or independent inputs and/or outputs. In this paper some methods which are usab...

Data Envelopment Analysis (DEA) has been applied in many efficiency studies in the banking sector. Conventional DEA models consider the system as a single-process black box. There are however a number of so-called network DEA approach that consider the system as composed by distinct processes or stages, each one with its own inputs and outputs and with intermediate flows among the stages. In th...

Efficiency aggregation and efficiency decomposition are two techniques used in modeling decision making units (DMUs) with two-stage network structures under network data envelopment analysis (DEA). Multiplicative efficiency decomposition (MED) is usually used in a very specialized two-stage structure when constant returns to scale (CRS) is assumed. MED-based network DEA retains the property of ...