The directional hybrid measure of efficiency in data envelopment analysis
محورهای موضوعی : History and biographyA. Mirsalehy 1 , M. Rizam Abu Baker 2 , L. S. Lee 3 , Gh. R. Jahanshahloo 4
1 - Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
2 - Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
3 - Laboratory of Computational Statistics and Operations Research, Institute of Mathematical
Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
4 - Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
کلید واژه: Data envelopment analysis, Directional distance function, Hybrid model, Efficiency score,
چکیده مقاله :
The efficiency measurement is a subject of great interest. The majority of studies on DEA models have been carried out using radial or non-radial approaches regarding the application of DEA for the efficiency measurement. This paper, based on the directional distance function, proposes a new generalized hybrid measure of efficiency under generalized returns to scale with the existence of both radial and non-radial inputs and outputs. It extends the hybrid measure of efficiency from Tone (2004) to a more general case. The proposed model is not only flexible enough for the decision-maker to adjust the radial and non-radial inputs and outputs to attain the efficiency score but also avoids the computational and interpretive difficulties, thereby giving rise to an important clarification and understanding of the generalized DEA model. Furthermore, several frequently-used DEA models (such as the CCR, BCC, ERM and SBM models) which depend on the radial or non-radial approaches are derived while their results were compared to the ones obtained from this hybrid model. The empirical examples emphasize the consequence of the proposed measure.
[1] R. D. Banker, A. Charnes, and W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30 (9) (1984), 1078-1092.
[2] R. G. Chambers, Y. Chung and R. Fare, Benefit and distance functions. Journal of Economic Theory, 70 (2) (1996), 407-419.
[3] R. G. Chambers, Y. Chung and R. Fare, Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98 (2) (1998), 351-364.
[4] A. Charnes, W. W. Cooper, Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9 (3-4) (1962), 181-186.
[5] A. Charnes, W. W. Cooper, B. Golany, L. Seiford and J. Stutz, Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30 (1) (1985), 91-107.
[6] A. Charnes, W. W. Cooper, and E. Rhodes, Measuring the efficiency of decision making units. European Journal of Operational Research, 2 (6) (1978), 429-444.
[7] W. D. Cook, L. M. Seiford, Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research, 192 (1) (2009), 1-17.
[8] W. W. Cooper, K. S. Park, and J. T. P. Ciurana, Marginal rates and elasticities of substitution with additive models in DEA. Journal of Productivity Analysis, 13 (2) (2000), 105-123.
[9] W. W. Cooper, K. S. Park and G. Yu, IDEA and AR-IDEA: Models for dealing with imprecise data in DEA. Management Science, 45 (4) (1999), 597-607.
[10] W. W. Cooper, L. M. Seiford and J. Zhu, Data envelopment analysis: History, models, and interpretations. Handbook on data envelopment analysis, Springer, (2011),1-39.
[11] A. Emrouznejad, B. R. Parker and G. Tavares, Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42 (3) (2008), 151-157.
[12] R. Fare, S. Grosskopf, A nonparametric cost approach to scale efficiency. The Scandinavian Journal of Economics, (1985), 594-604.
[13] M. J. Farrell, The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120 (3) (1957), 253-290.
[14] T. C. Koopmans, Analysis of production as an efficient combination of activities. Activity Analysis of Production and Allocation, 13 (1951), 33-37.
[15] S. Lertworasirikul, P. Charnsethikul and S. C. Fang, Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale. Computers & Industrial Engineering, 61 (4) (2011), 1017-1023.
[16] J. S. Liu, L. Y. Y Lu, W. M. Lu and B. J. Y. Lin, Data envelopment analysis 1978-2010: A citation-based literature survey. Omega, 41 (1) (2013), 3-15.
[17] D. G. Luenberger, Benefit functions and duality. Journal of Mathematical Economics, 21 (5) (1992), 461-481.
[18] D. G. Luenberger, Microeconomic theory. 486 McGraw-Hill New York, 1995.
[19] J. T. Pastor, J. L. Ruiz and I. Sirvent, An enhanced DEA Russell graph efficiency measure. 115 (1999), 596-607.
[20] J. T. Pastor, J. L. Ruiz and I. Sirvent, An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research,115 (3) (1999), 596-607.
[21] J. T. Pastor, J. L. Ruiz and I. Sirvent, Statistical test for detecting in uential observations in DEA. European Journal of Operational Research,115 (3) (1999), 542-554.
[22] V. V. Podinovski, Bridging the gap between the constant and variable returns-to-scale models: selective proportionality in data envelopment analysis. Journal of the Operational Research Society, 55 (3) (2004), 265-276.
[23] R. R. Russell, Measures of technical efficiency. Journal of Economic Theory, 35 (1) (1985), 109-126.
[24] L. M. Seiford, R. M. Thrall, Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of Econometrics, 46 (1) (1990), 7-38.
[25] R. W. Shepherd, Theory of cost and production functions. Princeton University Press, 2015.
[26] K. Tone, A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130 (3) (2001), 498-509.
[27] K. Tone, A hybrid measure of efficiency in DEA. GRIPS Research Report Series, 2004.