A New Mixed Integer Linear Model for Selecting the Most BCC-efficient DMU

Document Type : Research Paper

10.22059/imj.2012.72258

Abstract

Finding a single efficient decision making unit (DMU) as the most efficient unit has interested in some situations, by decision maker (manager). Conventional data envelopment analysis (DEA) assists decision maker in distinguishing between efficient and inefficient units. However, DEA does not provide more information about the efficient DMUs. Hence, several methods were introduced to find the best single efficient DMU. Foroughi [9] introduce a mixed integer linear model for selecting the best decision making unit in constant returns to scale situation. In this paper, we extend this model and formulate a new mixed integer linear model for determining most BCC-efficient DMU by solving only one linear programming which is useful for variable returns to scale situation, so has wider range of application in the management and industrial affairs than previous model. The applicability of proposed model is illustrated, using a real data set consisting 19 facility layout designs

Keywords

References

Amin, G. R., Toloo, M. (2007). Finding the most efficient DMUs in DEA: An improved integrated model. Computer & industrial engineering, 52(2), 71-77.
Amin, G. R (2009). Comment on finding the most efficient DMUs in DEA: An improved integrated model. Computer & industrial engineering, 56, 1701-1702.
Andersen, P., Petersen, N. C. (1993). A procedure for ranking efficient units in dataenvelopment analysis. Management Science, 39(10), 1261–1294.
Banker, R. D., Charnes, A., Cooper, W. W. (1984).Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational research 2(6), 429-444.
Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in DEA. Journal ofthe Operations Research Society, 39, 563–576.
Ertay, T., Ruan, D., & Tuzkaya, U. R. (2006). Integrating data envelopment analysisand analytic hierarchy for the facility layout design in manufacturing systems.Information Sciences, 176, 237–262.
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A 120, 253-290.
Foroughi, A. (2011). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computer & industrial engineering, 60(4), 550-554.
Karsak, E. E. &Ahiska, S. S (2008). Improved common weight MCDM model for technology selection. International journal of Production Research, 46(24), 6933-6944.
Karsak, E. E. & Ahiska, S. S. (2005). Practical common weight multi-criteria decision-making approach with improved discriminating power for technology selection.International Journal of Production Research, 43(8), 1537-1554.
Li, X. B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopmentanalysis. European Journal of Operational Research, 115, 507–517.
Roll, Y., Golany, B. (1993). Alternate methods of treating factor weights in DEA.Omega, 21, 99–109.
Sexton, T. R., Silkman, R. H., Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. In R. H. Silkman (Ed.), Measuring efficiency: Anassessment of data envelopment analysis. San Francisco, CA: Jossey -Bass.
Thompson, R. G., Langemeier, L. N., Lee, C. T., Thrall, R. M. (1990). The role ofmultiplier bounds in efficiencyanalysis with application to Kansas farming.Journal of Econometrics, 46, 93–108.
Thompson, R. G., Singleton, F. D., Thrall, R. M., Smith, B. A. (1986). Comparativesite evaluations for locating a highenergy physics lab in Texas. Interfaces, 16, 35–49.
Toloo, M., Nalchigar, S. (2008). A new integrated DEA model for finding most BCC-efficient DMU. Applied mathematical modelling, 33, 597-604.
Industrial Management Journal
Volume 4, Issue 1
April 2012
Pages 37-50

Files

Share

How to cite

Statistics