Input congestion, technical ineciency and output reduction in fuzzy data envelopment analysis
محورهای موضوعی : Applied Mathematics
M. khodabakhshi
1
(Department of Mathematics, Faculty of Science, Lorestan University, Khorram Abad, Iran.)
N. Aryavash
2
(Department of Mathematics, Faculty of Science, Lorestan University, Khorram Abad, Iran.)
کلید واژه: Fuzzy DEA, Congestion, Output reduction, Technical inefficiency,
چکیده مقاله :
During the last years, the concept of input congestion and technical ineff-ciency in data envelopment analysis (DEA), have been investigated by manyresearchers. The motivation of this paper is to present models which obtain thedecreased output value due to input congestion and technical ineciency. More-over, we extend the models to estimate input congestion, technical ineciencyand output reduction in fuzzy data envelopment analysis, by using the conceptof α-cut sets.
During the last years, the concept of input congestion and technical ineff-ciency in data envelopment analysis (DEA), have been investigated by manyresearchers. The motivation of this paper is to present models which obtain thedecreased output value due to input congestion and technical ineciency. More-over, we extend the models to estimate input congestion, technical ineciencyand output reduction in fuzzy data envelopment analysis, by using the conceptof α-cut sets.
[1] M. Asgharian, M. Khodabakhshi, L. Neralic, Congestion in stochastic data envelopment
analysis: An input relaxation approach, International Journal of
statistic and Management System, 5 (2010), 84{106.
[2] R. D. Banker, A. Charnes, W. W. Cooper, Some models for estimating technical
and scale ineciencies in data envelopment analysis, Management Science 30
(1984), 1078{1092.
[3] A. Charnes, W. W. Cooper, E. Rhodes, Measuring the eciency of DMUs,
European Journal of Operational Research 2 (1978), 429-444.
[4] W. W. Cooper, R. G. Thompson, R. M. Thrall, Introduction: Extensions and
new developments in DEA, Annals of Operations Research 66 (1996), 3{46.
[5] W. W. Cooper, L. M. Seiford, J. Zhu, A uni ed additive model approach for
evaluating ineciency and congestion with associated measures in DEA, Socio-
Economic Planning Sciences 34 (2000), 1{25.
[6] W. W. Cooper, H. Deng, B. Gu, Sh. Li, R. M. Thrall, Using DEA to improve
the management of congestion in Chinese industries, Socio-Economic Planning
Sciences 35 (2001), 227{242.
[7] W. W. Cooper, B. Gu, Sh. Li, Comparisons and evaluations of alternative approaches
to the treatment of congestion in DEA, European Journal of Operational
Research 132 (2001), 62{74.
[8] W. W. Cooper, B. Gu, Sh. Li, Note: Alternative treatments of congestion
in DEA-A response to the cherchye, Kuosmanen and Post critique, European
Journal of Operational Research 132 (2001), 81{87.
[9] W. W. Cooper , L. M. Seiford, J. Zhu. (2001). Slacks and congestion: Response
to a comment by R. Fare and S. Grosskopf, Socio-Economic Planning Sciences
35 (2001) 205-215.
[10] W. W. Cooper, H. Deng, Zh. M. Huang, S. X. Li A one-model approach to
congestion in data envelopment analysis, Socio-Economic Planning Sciences 36
(2002), 231{238.
[11] R. Fare, L. Svensson, Congestion of factors of production, Econometrica 48
(1980), 1743{1753.
[12] R. Fare, S. Groskopf, Measuring congestion in production, Zeitschrift fur Na-
tionalokonomie 43 (1983), 257-271.
[13] R. Fare, S. Groossko , When can slacks be used to identify congestion? An
answer to W. W. Cooper, L. Seiford and J. Zhu, Socio-Economic Planning
Sciences 35 (2001), 217{221.
[14] G. R. Jahanshahloo, M. Khodabakhshi, Suitable combination of inputs for improving
outputs in DEA with determining input congestion-Considering textile
industry of China, Applied Mathematics and Computation 151 (2004), 263{273.
[15] G. R. Jahanshahloo, M. Khodabakhshi, Determining assurance interval for non-
Archimedean element in the improving outputs model in DEA, Applied Mathe-
matics and Computation 151 (2004), 501{506.
[16] M. Khodabakhshi, A one-model approach based on relaxed combinations of
inputs for evaluating input congestion in DEA, Journal of Computational and
Applied mathematics 230 (2009) ,443{450.
[17] M. Khodabakhshi, Estimating most productive scale size with stochastic data
in data envelopment analysis, Economic Modelling 26 (2009), 968-973.
[18] M. Khodabakhshi, M. Asgharian, An input relaxation measure of eciency in
stochastic data envelopment analysis, Applied Mathematical Modelling 33 (2009),
2010-2023.
[19] M. S. Saadati, A. Memariani, Reducing weight exibility in fuzzy DEA, Applied
Mathematicsand Computation 161 (2005), 611{622.
[20] L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets System
1 (1978), 3{28.
