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International Conference on Multiple Criteria Decision Making

MCDM 2009: Cutting-Edge Research Topics on Multiple Criteria Decision Making pp 753–760Cite as

Nonlinear Deterministic Frontier Model Using Genetic Programming

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 35))

Abstract

In economics, several parametric regression-based models have been proposed to measure the technical efficiency of decision making units (DMUs). However, the problem of misspecification restricts the use of these methods. In this paper, symbolic regression is employed to obtain the approximate optimal production function automatically using genetic programming (GP). Monte Carlo simulation is used to compare the performance of data envelopment analysis (DEA), deterministic frontier analysis (DFA) and GP-based DFA with respect to three different production functions and sample sizes. The simulated results indicated that the proposed method has better performance than that of others with respect to nonlinear production functions.

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References

  1. Cooper, W.W., Tone, K.: Measure of Inefficiency in Data Envelopment Analysis and Stochastic Estimation. European Journal of Operational Research 99, 72–88 (1997)

    Article  MATH  Google Scholar 

  2. Ruggiero, J.: A New Approach for Technical Efficiency Estimation in Multiple Output Production. European Journal of Operational Research 111, 369–380 (1998)

    Article  MATH  Google Scholar 

  3. Chen, T.Y.: A Comparison of Chance-Constrained DEA and Stochastic Frontier Analysis: Bank Efficiency in Taiwan. The Operational Research Society 53, 492–500 (2002)

    Article  MATH  Google Scholar 

  4. Schmidt, P.: Production Frontier Function. Econometric Reviews 4, 289–328 (1985)

    Article  MathSciNet  Google Scholar 

  5. Greene, W.: The Econometric Approach to Efficiency Analysis. In: Fried, H.O., Lovell, C.A.K., Schmidt, S.S. (eds.) The Measurement of Productive Efficiency: Techniques and Applications, pp. 68–119. Oxford University Press, New York (1993)

    Google Scholar 

  6. Giannakas, K., Tran, K.C., Tzouvelekas, V.: On the Choice of Functional Form in Stochastic Frontier Modeling. Empirical Economics 25, 75–100 (2003)

    Article  Google Scholar 

  7. Gonzalez, D.S., Castro, A.V.: Comparing Neural Networks and Efficiency Techniques in Non-linear Production Function. In: The Seventh European Workshop on Efficiency and Productivity Analysis at University of Oviedo, Spain (2001)

    Google Scholar 

  8. Wang, S.: Adaptive Non-parametric Efficiency Frontier Analysis a Neural-Network Based Model. Computers & Operations Research 30, 279–295 (2003)

    Article  MATH  Google Scholar 

  9. Wang, S.: Nonparametric Econometric Modelling: A Neural Network Approach. European Journal of Operational Research 89, 581–592 (1996)

    Article  MATH  Google Scholar 

  10. Castillo, F., Marshall, K., Green, J., Kordon, A.: A Methodology for Combining Symbolic Regression and Design of Experiments to Improve Empirical Model Building. In: Genetic and Evolutionary Computation Conference, pp. 1975–1985 (2003)

    Google Scholar 

  11. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  12. Davidson, J.W., Savic, D.A., Walters, G.A.: Symbolic and Numerical Regression: Experiments and Applications. Information Sciences 150, 95–117 (2003)

    Article  MathSciNet  Google Scholar 

  13. Huang, J.J., Tzeng, G.H., Ong, C.S.: Two-stage genetic programming (2SGP) for the credit scoring model. Applied Mathematics and Computation 174, 1039–1053 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Ong, C.S., Huang, J.J., Tzeng, G.H.: Building credit scoring models using genetic programming. Expert Systems with Applications 29, 41–47 (2005)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate Institute of Management of Technology, National Chiao Tung University, Hsinchu, Taiwan

    Chin-Yi Chen & Gwo-Hshiung Tzeng

  2. Department of Business Administration, Chung Yuan Christian University, Chung Li, Taiwan

    Chin-Yi Chen

  3. Department of Business and Entrepreneurial Administration, Kainan University, Taoyuan, Taiwan

    Jih-Jeng Huang & Gwo-Hshiung Tzeng

Authors
  1. Chin-Yi Chen
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  2. Jih-Jeng Huang
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  3. Gwo-Hshiung Tzeng
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nebraska, NE 68182, Omaha, USA

    Yong Shi

  2. Chinese Academy of Sciences, Beijing, P.R. China

    Shouyang Wang  & Jianping Li  & 

  3. University of Electronic Science and Technology of China, Chengdu, China

    Yi Peng  & Yong Zeng  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Chen, CY., Huang, JJ., Tzeng, GH. (2009). Nonlinear Deterministic Frontier Model Using Genetic Programming. In: Shi, Y., Wang, S., Peng, Y., Li, J., Zeng, Y. (eds) Cutting-Edge Research Topics on Multiple Criteria Decision Making. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02298-2_111

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  • DOI: https://doi.org/10.1007/978-3-642-02298-2_111

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02297-5

  • Online ISBN: 978-3-642-02298-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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