Skip to main content
Log in

A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment

  • Published:
Journal of Productivity Analysis Aims and scope Submit manuscript

Abstract

A three stage approach is proposed to measure technical efficiency in a fuzzy environment. This approach uses the traditional data envelopment analysis framework and then merges concepts developed in fuzzy parametric programming (Carlsson and Korhonen, 1986). In the first stage, vague input and output variables are expressed in terms of their risk-free and impossible bounds and a membership function. This membership function represents the degree to which a production scenario is plausible. In the second stage, conventional efficiency measurement models (Banker, Charnes and Cooper, 1984; Deprins, Simar and Tulkens, 1984) are re-formulated in terms of the risk-free and impossible bounds and the membership function for each of the fuzzy input and output variables. In the third stage, technical efficiency scores are computed for different values of the membership function so as to identify uniquely sensitive decision making units. The approach is illustrated in the context of a preprint and packaging manufacturing line which inserts commercial pamphlets into newspapers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Almond, R.G. (1995). “Discussion: Fuzzy Logic: Better Science Or Better Engineering?” Technometrics 37:3, 267–270.

    Google Scholar 

  • Athanassopoulos, A., and K. Triantis. (1998). “Assessing Aggregate Cost Efficiency and the Related Policy Implications for Greek Local Municipalities.” Forthcoming.

  • Banker, R. D., A. Charnes, and W. W. Cooper. (1984). “Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis.” Management Science 30:9, 1078–1092.

    Google Scholar 

  • Bazaraa, M. S., J. J. Jarvis, and H. D. Sherali. (1990). Linear Programming and Network Flows. New York: John Wiley & Sons.

    Google Scholar 

  • Bellman, R. E., and L. A. Zadeh. (1970). “Decision Making in a Fuzzy Environment.” Management Science 17:4, 141–164.

    Google Scholar 

  • Bonaplata, J. (1995). “Evaluation of Productivity and Quality Performance in the Newspaper Preprint Insertion Process.” Masters project and report, Virginia Tech, Department of Industrial and Systems Engineering, Blacksburg, Virginia.

    Google Scholar 

  • Carlsson, C., and P. Korhonen. (1986). “A Parametric Approach To Fuzzy Linear Programming.” Fuzzy Sets and Systems 20, 17–30.

    Google Scholar 

  • Charnes, A., W. W. Cooper, and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2, 429–444.

    Google Scholar 

  • Cooper, W. W., K. K. Sinha, and R. S. Sullivan. (1992). “Measuring Complexity in High-Technology Manufacturing: Indexes for Evaluation.” Interfaces 4:22, 38–48.

    Google Scholar 

  • Deprins, D., L. Simar, and H. Tulkens. (1984). “Measuring Labor Efficiency on Post Offices.” In Marchand, Pestieau, and Tulkens (eds.), The Performance of Public Enterprises: Concepts and Measurement. Amsterdam: North Holland, 243–267.

    Google Scholar 

  • Färe, R., S. Grosskopf, and C. A. K. Lovell. (1994). Production Frontiers. Cambridge: Cambridge University Press.

    Google Scholar 

  • Fried, H. O., C. A. K. Lovell, and S. S. Schmidt (eds.). (1993). The Measurement of Productive Efficiency: Techniques and Applications. New York: Oxford University Press.

    Google Scholar 

  • Girod, O. A. (1996). “Measuring Technical Efficiency in a Fuzzy Environment,” Doctoral (Ph.D.) dissertation, Virginia Tech, Department of Industrial and Systems Engineering, Blacksburg, Virginia.

    Google Scholar 

  • Girod, O. A., and K. Triantis. (1998). “The Evaluation of Productive Efficiency Using a Fuzzy Mathematical Programming Approach: The Case of the Newspaper Preprint Insertion Process.” Forthcoming. IEEE Transactions on Engineering Management.

  • Kosko, B. (1993). Fuzzy Thinking: The New Science of Fuzzy Logic. New York: Hyperion Publisher.

    Google Scholar 

  • Land, C. A. K. Lovell, and Thore. (1993). “Chance-Constrained Efficiency Analysis.” Managerial and Decision Economics 14, 541–553.

    Google Scholar 

  • Seaver, B. L., and K. P. Triantis. (1992). “A Fuzzy Clustering Approach Used in Evaluating Technical Efficiency Measures in Manufacturing.” Journal of Productivity Analysis 3, 337–364.

    Google Scholar 

  • Sengupta, J. K. (1987). “Data Envelopment Analysis for Efficiency Measurement in the Stochastic Case.” Computers in Operational Research 14:2, 117–129.

    Google Scholar 

  • Sengupta, J. K. (1992). “A Fuzzy Systems Approach in Data Envelopment Analysis.” Computers Math. Applic. 24:8/9, 259–266.

    Google Scholar 

  • Tulkens H. (1993). “On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts, and Urban Transit.” The Journal of Productivity Analysis 4, 183–210.

    Google Scholar 

  • Triantis, K. and R. McNelis. (1995). “The Measurement and Empirical Evaluation of Quality and Productivity for a Manufacturing Process: A Data Envelopment Analysis (DEA) Approach.” In R. Schraft, W. Sullivan, M. Ahmad, and H. Jacobi (eds.), Flexible Automation and Intelligent Manufacturing. New York: Begell House, 1134–1145.

    Google Scholar 

  • Zadeh, L. A. (1965). “Fuzzy Sets.” Information and Control 8, 338–353.

    Google Scholar 

  • Zimmermann, H. J. (1986). Fuzzy Set Theory and Mathematical Programming, Fuzzy Set Theory and Applications. Riedel Publishing Company.

  • Zimmermann, H. J. (1991). Fuzzy Set Theory and Its Applications, Second Edition. Boston: Kluwer Academic Publishers.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Triantis, K., Girod, O. A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment. Journal of Productivity Analysis 10, 85–102 (1998). https://doi.org/10.1023/A:1018350516517

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1018350516517

Navigation