Network DEA II

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 208)

Abstract

The original DEA model by Charnes et al. (Eur. J. Oper. Res. 2:429–444, 1978) is set to analyze production as a black box, i.e., there is no information about the processes inside. Network DEA was proposed for analysis of the contents of the black box. This theory allows the researcher to model processes within the black box by formulating sub-technology DEA models. The interaction of sub-technology DEA models preserves the DEA structure, and the network model can therefore be solved using linear programming. This chapter discusses network DEA models, both static and dynamic. The discussion also explores various useful objective functions that can be applied to the models to find the optimal allocation of resources for processes within the black box that are normally invisible to DEA.

Keywords

Data Envelopment Analysis (DEA) Network Intermediate products Dynamic production 

References

  1. Bogetoft, P., & Wang, D. (2005). Estimating the potential gains from mergers. Journal of Productivity Analysis, 23(2), 145–171.CrossRefGoogle Scholar
  2. Chambers, R. G., Chung, Y., & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98(2), 351–364.CrossRefGoogle Scholar
  3. Charnes, A., Copper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  4. Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170–1176.CrossRefGoogle Scholar
  5. Cook, W. D., Liang, L., & Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, 38(6), 423–430.CrossRefGoogle Scholar
  6. Cooper, W. W., Seiford, L., & Zhu, J. (2004). Handbook on data envelopment analysis. Boston: Kluwer Academic.Google Scholar
  7. Fallah-Fini, S., Triantis, K., & Johnson, A. L. (2013). Reviewing the literature on non-parametric dynamic efficiency measurement: state-of-the-art. Journal of Productivity Analysis, in print.Google Scholar
  8. Färe, R., Grosskopf, S., & Brännlund R. et al. (1996a). Intertemporal production frontiers: With dynamic DEA. Boston/London/Dordrecht: Kluwer Academic.Google Scholar
  9. Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65–70.CrossRefGoogle Scholar
  10. Färe, R., & Grosskopf, S. (1997). Efficiency and productivity in rich and poor countries. In B. S. Jensen & K. Wong (Eds.), Dynamics, economic growth, and international trade, (pp. 243–263). Ann Arbor: University of Michigan Press, Studies in International Economics.Google Scholar
  11. Färe, R., & Grosskopf, S. (2004). New directions: Efficiency and productivity. Boston: Kluwer Academic.Google Scholar
  12. Färe, R., & Grosskopf, S. (2010). Directional distance functions and slacks-based measures of efficiency. European Journal Of Operational Research, 200(1), 320–322.CrossRefGoogle Scholar
  13. Färe, R., & Lovell, C. A. K. (1978). Measuring the technical efficiency of production. Journal of Economic Theory, 19(1), 150.CrossRefGoogle Scholar
  14. Färe, R., Grosskopf, S., & Li, S.-K. (1992). Linear programming models for firm and industry performance. Scandinavian Journal of Economics, 94(4), 599–608.CrossRefGoogle Scholar
  15. Färe, R., Grosskopf, S., & Whittaker, G. (2007a). Network DEA. In J. Zhu & W. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209–240). New York: Springer.CrossRefGoogle Scholar
  16. Färe, R., Grosskopf, S., Zelenyuk, V., Färe, R., Grosskopf, S., & Zelenyuk, V. (2007b). Finding common ground: Efficiency indices. In R. Färe, S. Grosskopf, D. Primont, R. Färe, S. Grosskopf, & D. Primont (Eds.), Aggregation, efficiency, and measurement (pp. 83–95). New York: Springer.CrossRefGoogle Scholar
  17. Färe, R., Grosskopf, S., & Margaritis, D. (2011). Coalition formation and data envelopment analysis. Journal of Centrum Cathedra, 4, 216–223.CrossRefGoogle Scholar
  18. Färe, R., Grosskopf, S., & Whittaker, G. (2013). Directional output distance functions: endogenous direction based on exogenous normalization constraints. Journal of Productivity Analysis, 40(3), 267–269.Google Scholar
  19. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253–281.CrossRefGoogle Scholar
  20. Jaenicke, E. C. (2000). Testing for intermediate outputs in dynamic DEA models: Accounting for soil capital in rotational crop production and productivity measures. Journal of Productivity Analysis, 14(3), 247–266.CrossRefGoogle Scholar
  21. Johansen, L. (1972). An Integration of micro and macro short run and long run aspects. Amsterdam: North-Holland Publishing.Google Scholar
  22. Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962.CrossRefGoogle Scholar
  23. Kemeny, J. G., Morgenstern, O., & Thompson, G. L. (1956). A generalization of the von Neumann model of an expanding economy. Econometrica, 24(2), 115–135.CrossRefGoogle Scholar
  24. Luenberger, D. G. (1995). Microeconomic theory. New York: McGraw-Hill.Google Scholar
  25. Nemota, J., & Goto, M. (1999). Dynamic data envelopment analysis: Modeling intertemporal behavior of a firm in the presence of productive inefficiencies. Economic Letters, 64, 51–56.CrossRefGoogle Scholar
  26. Nemota, J., & Goto, M. (2003). Measuring dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities. Journal of Productivity Analysis, 19, 191–210.CrossRefGoogle Scholar
  27. Shephard, R. W. (1953). Cost and production functions. Princeton: Princeton University Press.Google Scholar
  28. Shephard, R. W. (1970). Theory of cost and production functions. Princeton: Princeton University Press.Google Scholar
  29. Shephard, R. W., & Färe, R. (1974). The law of diminishing returns. Zeitschrift für Nationalökonomie Journal of Economics, 34(1–2), 69–90.Google Scholar
  30. Shephard, R. W., & Färe, R. (1975). A dynamic theory of production correspondences. ORC 75–13, Berkeley: Operations Research Center, University of California.Google Scholar
  31. Shephard, R. W., & Färe, R. (1980). Dynamic theory of production correspondences. Königstein: Verlag Anton Hain.Google Scholar
  32. Tone, K. (2001). Slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.CrossRefGoogle Scholar
  33. Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Rolf Färe
    • 1
  • Shawna Grosskopf
    • 2
    • 3
  • Gerald Whittaker
    • 4
  1. 1.Economics Unit and Department of Applied EconomicsOregon State UniversityCorvallisUSA
  2. 2.Department of EconomicsOregon State UniversityCorvallisUSA
  3. 3.CEREUmeaSweden
  4. 4.National Forage Seed Production Research Center, Agricultural Research ServiceUSDACorvallisUSA

Personalised recommendations