Using Neural Network to Determine Input Excesses, Output Shortfalls and Efficiency of Dmus in Russell Mode

Subject Areas : Statistics

D. Modhej ¹ , M. Sanei ² , N. Shoja ³

1 - Departments of Applied Mathematics, Islamic Azad University, Central Tehran Branch, Tehran, Iran
2 - Departments of Applied Mathematics, Islamic Azad University, Central Tehran Branch, Tehran, Iran Corresponding author
3 - Department of Mathematics, Firoozkooh branch, Islamic Azad University, Firoozkooh, Iran

Received: 2015-11-10 Accepted : 2016-02-10 Published : 2016-02-01

Keywords: شبکه عصبی مصنوعی, تحلیل پوششی داده ها, مدل راسل, کارائی, انقباض ورودی, انبساط خروجی,

Abstract :

Data Envelopment Analysis (DEA) has two fundamental approaches for assessing theefficiency with different characteristics; radial and non-radial models. This paper isconcerned the non-radial model of Russell which is a non linear model. Conventional DEAfor a large dataset with many inputs/outputs would require huge computer resources in termsof memory and CPU time. Artificial Neural Network (ANN) is one of the most populartechniques for non linear models and for measuring the relative efficiency of a large datasetwith many inputs/ outputs. Also in the last decade researches focused on efficiencyevaluation via DEA as well as using ANN. In this paper we will estimate the input excessesand the output shortfalls in addition to efficiency of Decision Making Units (DMUs) inRussell model through ANN. The proposed integrated approach is applied to an actualIranian bank set; the result indicates that it yields a satisfactory solution.works.

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