LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Article
Subjects:
Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models - fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Ahn, T., Charnes, A., & Cooper, W. W. (1988). Efficiency characterizations in different DEA models. Socio-Economic Planning Sciences, 22(6), 253-257.
    • Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261-1264.
    • Azadeh, A., Moghaddam, M., Asadzadeh, S. M., & Negahban, A. (2011). An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: The case of injection process with ambiguous data. European Journal of Operational Research, 214(3), 768-779.
    • Azadeh, A., Sheikhalishahi, M., & Asadzadeh, S. M. (2011). A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity. Renewable Energy, 36(12), 3394-3401.
    • Bagherzadeh valami, H. (2009). Cost efficiency with triangular fuzzy number input prices: An application of DEA. Chaos, Solitons & Fractals, 42(3), 1631-1637.
    • 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.
    • Chen, Y. (2005). Measuring super-efficiency in DEA in the presence of infeasibility. European Journal of Operational Research, 161(1), 447-468.
    • Cook, W. D., Liang, L., Zha, Y., & Zhu, J. (2008). A modified super-efficiency DEA model for infeasibility. Journal of the Operational Research Society, 60(2), 276-281.
    • Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver Software. Second editions. Springer, ISBN, 387452818, 490.
    • Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring labor-efficiency in post offices (M. Marchand, P. Pestieau, H. Tulkens ed.). North-Holland, Amsterdam: The Performance of Public Enterprises: Concepts and Measurment Emrouznejad, A. & M. Tavana (2014). Performance Measurement with Fuzzy Data Envelopment Analysis. In the series of “Studies in Fuzziness and Soft Computing”, Springer-Verlag, ISBN 978-3-642-41371-1.
    • Ghasemi, M. R., Ignatius, J., & Davoodi, S. M. (2014a). Ranking of Fuzzy Efficiency Measures via Satisfaction Degree. In Performance Measurement with Fuzzy Data Envelopment Analysis (pp. 157-165). Springer Berlin Heidelberg.
    • Ghasemi, M. R., Ignatius, J., & Emrouznejad, A. (2014b). A bi-objective weighted model for improving the discrimination power in MCDEA. European Journal of Operational Research, 233(3), 640-650.
    • Guo, P., & Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets and Systems, 119(1), 149-160.
    • Hasuike, T. (2011). Technical and cost efficiencies with ranking function in fuzzy data envelopment analysis. In Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on (Vol. 2, pp. 727-730).
    • Hatami-Marbini, A., Emrouznejad, A., & Tavana, M. (2011a). A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making. European Journal of Operational Research, 214(3), 457-472.
    • Hatami-Marbini, A., Saati, S., & Tavana, M. (2011b). Data Envelopment Analysis with Fuzzy Parameters: An Interactive Approach. International Journal of Operations Research and Information Systems, 2(3), 39-53.
    • Hatami-Marbini, A., Tavana, M., & Ebrahimi, A. (2011c). A fully fuzzified data envelopment analysis model. International Journal of Information and Decision Sciences, 3(3), 252-264.
    • Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy Sets and Systems, 47(1), 81-86.
    • Jahanshahloo, G. R., Hosseinzadeh Lotfi, F., Rostamy Malkhalifeh, M., & Ahadzadeh Namin, M. (2009). A generalized model for data envelopment analysis with interval data. Applied Mathematical Modelling, 33(7), 3237-3244.
    • Khodabakhshi, M., Gholami, Y., & Kheirollahi, H. (2010). An additive model approach for estimating returns to scale in imprecise data envelopment analysis. Applied Mathematical Modelling, 34(5), 1247-1257.
    • Lee, H.-S., Chu, C.-W., & Zhu, J. (2011). Super-efficiency DEA in the presence of infeasibility. European Journal of Operational Research, 212(1), 141-147.
    • León, T., Liern, V., Ruiz, J. L., & Sirvent, I. (2003). A fuzzy mathematical programming approach to the assessment of efficiency with DEA models. Fuzzy Sets and Systems, 139(2), 407-419.
    • Lertworasirikul, S., Fang, S.-C., A. Joines, J., & L.W. Nuttle, H. (2003). Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy Sets and Systems, 139(2), 379-394.
    • Liu, Y.-K., & Liu, B. (2003). Expected value operator of random fuzzy variable and random fuzzy expected value models. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(2), 195-215.
    • Muren, Ma, Z., & Cui, W. (2012). Fuzzy data envelopment analysis approach based on sample decision making units. Journal of Systems Engineering and Electronics, 23(3), 399-407.
    • Puri, J., & Yadav, S. P. (2012). A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector. Expert Systems with Applications, 40(5), 1437-1450.
    • Qin, R., & Liu, Y.-K. (2010). A new data envelopment analysis model with fuzzy random inputs and outputs. Journal of Applied Mathematics and Computing, 33(1-2), 327-356.
    • Qin, R., Liu, Y., & Liu, Z.-Q. (2011). Modeling fuzzy data envelopment analysis by parametric programming method. Expert Systems with Applications, 38(7), 8648-8663.
    • Qin, R., Liu, Y., Liu, Z., & Wang, G. (2009). Modeling fuzzy DEA with Type-2 fuzzy variable coefficients. In Advances in Neural Networks-ISNN 2009 (pp. 25-34): Springer.
    • Soleimani-damaneh, M. (2009). Establishing the existence of a distance-based upper bound for a fuzzy DEA model using duality. Chaos, Solitons & Fractals, 41(1), 485-490.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article