An Application of Linear Programming in the Estimation of Technical Efficiency of DMU
B.Venkateswarlu1, B. Mahaboob2, K.A. Azmath3, C. Narayana4, C. Muralidaran5

1Dr B. Venkateswarlu, School of Advanced Science (SAS), Vellore Institute of Technology, Vellore, Tamil nadu, India.
2Dr B. Mahaboob, Department of Mathematics, Koneru Lakshmaiah Educational Foundation, Vaddeshwaram, Guntur, A.P., India.
3Dr K.A. Azmath, Department of Mathematics, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh.
4Dr C. Narayana, Mathematics Department, Sri Harsha institute of P.G Studies, Nellore, Andhra Pradesh.
5C. Muralidaran, School of Advanced Science (SAS), Vellore Institute of Technology, Vellore, Tamil nadu.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1956-1959| Volume-8 Issue-6, August 2019. | Retrieval Number:F7931088619/2019©BEIESP | DOI: 10.35940/ijeat.F7931.088619
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Abstract: The main objective of this research article is to propose linear programming problems for estimating the technical efficiency of DMU. This research article deals with the Shepard’s [1] input distance function and its properties are also evaluated. In addition to these extreme efficiency, efficiency but not extreme, weak efficiency and inefficient of a DMU are specifically examined here. In DEA the nature of returns to scale can be inferred. But we cannot quantify the returns to scale. The computations for the classification of RTS of a DMU are also derived in this discourse. In 2009, Barbara A. Mark [2] in their paper, depicted an innovative method which is non-parametric to estimate technical efficiency. In 2011 S. Nuti [3] inquired into the interrelations among technical efficiency scores, weighted per capita cost and overall performance. Gahe Zing Samuel Yannik [4] used DEA to calculate technical assessment in banking sectors. In 2015 Smita Verma and others chosen a random sample of ten textile mills in India over the time period 2011-2013 and measures its technical efficiency using Data Envelopment Analysis
Keywords: DMU (Decision Making Unit), input distance function, index set, DEA (Data Envelopment Analysis), VRTS (Variable Returns to Scale), IDF (Input Distance Function).