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Modeling Fuzzy DEA with Type-2 Fuzzy Variable Coefficients

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Book cover Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

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Abstract

Data envelopment analysis (DEA) is an effective method for measuring the relative efficiency of a set of homogeneous decision-making units (DMUs). However, the data in traditional DEA model are limited to crisp inputs and outputs, which cannot be precisely obtained in many production processes or social activities. This paper attempts to extend the traditional DEA model and establishes a DEA model with type-2 (T2) fuzzy inputs and outputs. To establish this model, we first propose a reduction method for T2 fuzzy variables based on the expected value of fuzzy variable. After that, we establish a DEA model with the obtained fuzzy variables. In some special cases such as the inputs and outputs are independent T2 triangular fuzzy variables, we provide a method to turn the original DEA model to its equivalent one. At last, we provide a numerical example to illustrate the efficiency of the proposed DEA model.

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Author information

Authors and Affiliations

  1. College of Mathematics & Computer Science, Hebei University Baoding, 071002, Hebei, China

    Rui Qin, Yankui Liu & Guoli Wang

  2. School of Creative Media, City University of Hong Kong, Hong Kong, China

    Zhiqiang Liu

Authors
  1. Rui Qin
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  2. Yankui Liu
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  3. Zhiqiang Liu
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  4. Guoli Wang
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Qin, R., Liu, Y., Liu, Z., Wang, G. (2009). Modeling Fuzzy DEA with Type-2 Fuzzy Variable Coefficients. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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