Data Envelopment Analysis with LINGO Modeling for Technical Educational Group of an Organization

Subject Areas : International Journal of Data Envelopment Analysis

Mohd Rizwanullah ¹

1 - Associate Professor, Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, India

Received: 2016-10-04 Accepted : 2016-10-04 Published : 2016-01-01

Keywords: Data envelopment analysis (DEA, Efficiency, Performance, Reference Units, ,

Abstract :

Data Envelopment Analysis (DEA) was developed to help compare the relative performance of decision-making units. It is a non-parametric method for performing frontier analysis. It uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics [3]. DEA generates an efficiency score between 0 and 1 for each unit, indicating how effectively they are managing their resources. A compelling feature of DEA is it develops a unique rating system for each unit designed to make them look their best. This should facilitate acceptance of DEA within an organization [2]. LINGO is a comprehensive tool designed to make building and solving mathematical optimization models easier and more efficient. In this paper, we have explained what DEA does? How DEA evaluates efficiency, how DEA identifies paths to improve efficiency using this LINGO software, and how to use DEA. We have considered a group of 3 technical colleges (campuses). We assume that each campus “converts” 2 inputs into 3 outputs. This will enable managers to explore and assess the value of using DEA in their service operations. This paper also shows that Lingo Model can offer more information than the standard LP approach with exclusion of Simulation modeling approach [4]. The advantages of this approach are modesty, flexibility and visualization. The results by comparison of educational campuses were helpful for organization management.

References:

[1] Roll, Y., W.D. Cook, and B. Golany. Controlling factor weights in data envelopment analysis. IIE Transactions 1991; 23: 2-9.

[2] Bardhan, I., W.F. Bowlin, W.W. Cooper and T. Sueyoshi. Models and Measures for Efficiency Dominance in DEA, Part I: Additive Models and MED Measures. Journal of the Operational Research Society of Japan1996; 39: 322-332.

[3] Green, R.H.. DIY DEA: Implementing Data Envelopment Analysis in the Mathematical Programming Language AMPL. Omega 1996; 24(4): 489- 495.