Single Model Approach to Evaluate Optimistic and Pessimistic Congestion in Fuzzy Data Envelopment Analysis

Volume 9, Issue 4, pp 389 - 400 http://dx.doi.org/10.22436/jmcs.09.04.15

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Authors

Fatemeh Baghbani Abootaleb - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. Alireza Alinezhad - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. Mehdi Yazdani - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

Data Envelopment Analysis (DEA) is an almost new data oriented approach for measuring the performance of series of homogenous entities named Decision Making Unit (DMU) with the ability to alter multiple inputs into multiple outputs. This article investigates the development of congestion model with regard to fuzzy input and output. Also, a new concept called as optimistic and pessimistic are introduced for congestion model. To create this concept, the upper and lower bound of efficiency, outputs and inputs are required. This concept is provided with α-cut approach in fuzzy data envelopment analysis (FDEA). In order to evaluate the efficiency of given models, data of some universities of Iran are applied. The results reveal the proper validity of proposed model.

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Keywords

• Data envelopment analysis
• fuzzy inputs and outputs
• α-cut approach
• optimistic and pessimistic congestion.

MSC

•  90B50
•  90C70
•  90C05

References

• [1] A. Charnes, W. W. Cooper, E. Rhodes, Measuring the efficiency of decision making units, European Journal of the Operational research, 2 (1978), 429-444.
  • View Article
  • MathSciNet
  • Google Scholar


• [2] R. D. Banker, A. Charnes, W. W. Cooper, Some model for estimating technical and scale inefficiencies in data envelopment analysis, Management science, 30 (1984), 1078-1092.
  • View Article
  • Google Scholar


• [3] A. Alinezhad, M. Zohrebandian, N. Esfandiary, Introduction on performance measurement systems, Azad university of Qazvin, Publication of Azad University (2011)


• [4] W. W. Cooper, K. S. Park, G. Yu, IDEA and AR-IDEA, Models for dealing with imprecise data in DEA, Management Science, 45 (1999), 597-607.
  • View Article
  • Google Scholar
  • MATH


• [5] W. W. Cooper, H. Deng, B. Gu, S. X. Li, R. M. Thrall, Using DEA to improve the management of congestion in Chinese industries, Socio- Economic Planning Science, 35 (2001), 1981-1997.
  • View Article
  • Google Scholar


• [6] J. Zhu , Imprecise data envelopment analysis (IDEA) A review and improvement with an application, European Journal of Operational Research, 144 (2003), 513-529.
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [7] L. Pour Karimi , Introduction of congestion concept based on production function in data envelopment analysis, MSc thesis, applied mathematics (Operation research), Tarbiat Moalem University (2005)


• [8] R. Fare, S. Grosskopf, CAK. Lovell, The measurement of efficiency of production Boston, MN:Klewer-Nijhoff publishing, (1985)
  • Google Scholar


• [9] W. W. Cooper, R. G. Thompson, R. M. Thrall, Introductions and new developments in DEA, Annals of Operations Research, 66 (1996), 3-45.
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [10] H. Zarehaghighi, Study on congestion evaluation methods in data envelopment analysis, Applied mathematics (operation research), MSc thesis, Tarbiat MoalemUniversity, (2010),


• [11] P. L. Brockett, W. W. Cooper, H. C. Shin, Y. Wang, Inefficiency and congestion in Chinese production before and after the 1978 economic reforms, Socio-Economic Planning Sciences, 3 (1988), 1-20.
  • View Article
  • Google Scholar


• [12] W. W. Cooper, L. M. Seiford, J. Zhu, A unified additive model approach for evaluating efficiency and congestion with associated measures in DEA, Socio-Economic Planning Sciences, 34 (2000), 1-25.
  • View Article
  • Google Scholar


• [13] K. Tone, B. K. Sahoo, Degree of scale economics and Congestion: A unified DEA approach, European Journal of Operational Research, 158 (2004), 755-772.
  • View Article
  • Google Scholar


• [14] G. R. Jahanshahloo, M. Khodabakhshi, Suitable combination of input for improving output in DEA with determining input congestion considering textile industry of china, Applied Mathematics and Computation, 151 (2004), 263-273.
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [15] M. Khodabakhshi, A one model approach based on relaxed combination of inputs for evaluating input congestion in DEA, Journal of computational Applied Mathematics, 230 (2009), 443-450.
  • View Article
  • MathSciNet
  • Google Scholar


• [16] S. Fanati Rashidi , A new method for measuring congestion in data envelopment analysis, PHD thesis, Azad university of OloomTahghighat, Tehran (2010)
  • View Article
  • Google Scholar


• [17] L. A. Zadeh, Fuzzy sets, Inform and control, (1965), 338-353.
  • Google Scholar


• [18] M. Yazdan Panah Mariki, Congestion calculating in Fuzzy data envelopment analysis, calculating in Fuzzy data envelopment analysis, MSc thesis, Applied Mathematics, Azad university of Broujerd (2012)


• [19] M. S. Saati, A. Memariani, G. R. Jahanshahloo, Efficiency analysis and Ranking of DMUs with Fuzzy Data, Fuzzy Optimization and Decision Making, (2002), 255-267.
  • View Article
  • Google Scholar


• [20] W. W. Cooper, B. Gu, S. Li, Note:Alternative treatments of congestion in DEA a response to the Cherchye, Kuosmonen and post critique, European journal of operational research, 132 (2001), 81-87.
  • View Article
  • Google Scholar