DEA with Missing Data: An Interval Data Assignment Approach

Authors

1 Associate Professor, Department of Mathematics, Islamic Azad University, Karaj Branch, Karaj, Iran

2 MSc, Department of Mathematics, Islamic Azad University, Karaj Branch, Karaj , Iran

Abstract

In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the proposed approach suggests using an acceptable range for missing inputs and outputs, which is determined by the decision maker (DM). Then, applying the least favourable bounds of missing data along with using the proposed range is suggested in estimating the production frontier.  A data set is used to illustrate the approach.

Keywords

References

Azizi H. (2013), “A note on data envelopment analysis with missing values: an interval DEA approach”, The International Journal of Advanced Manufacturing Technology, 66(9-12), 1817-1823.

Banker, RD, Charnes, A., Cooper, WW. (1984), “Models for Estimation of Technical and Scale Inefficiencies in Data Envelopment Analysis”, Management Sience, 30, 1078-1092.

Charnes A, Cooper WW. (1962), “Programming with linear fractional functional”, Naval Research Logistics Quarterly, 9, 181-185.

Charnes A, Cooper WW, Rhodes E. (1978), “Measuring the efficiency of decision making units”, European journal of operational research, 2(4), 429 - 444.

Cooper WW, Seiford LM, Tone K. (2000), Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, Boston.

Farrell MJ. (1957), “The Measurement of Productive Efficiency”, Journal of Royal Statistical Society, 120(3), 253-281.

Kao C, Liu ST, (2000), “Data envelopment analysis with missing data: An application to University Libraries in Taiwan”, Journal of the Operational Research Society, 51 (8), 897–905.

Kuosmanen, T. (2009), Data envelopment analysis with missing data, Journal of the Operational Research Society, 60, 1767-1774.

Neal, PVO, Ozcan, YA, Yanqiang, M. (2002), “Benchmarking mechanical ventilation services in teaching hospitals”, Journal of Medical Systems. 26(3), 227–240.

Smirlis YG, Maragos EK, Despotis DK. (2006), “Data envelopment analysis with missing values: An interval DEA approach”. Applied Mathematics and Computation, 177(1), 1-10.

Zha Y, Song A, Xu Ch, Yang H. (2013), “Dealing with missing data based on data envelopment analysis and halo effect”, Applied Mathematical Modelling, 37(9), 6135–6145.

 

Journal of Optimization in Industrial Engineering

Volume 8, Issue 17
Winter and Spring 2015
Pages 31-36

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