Skip to main content
Log in

Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions

  • Short Note
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Computing directional distance functions for a free disposal hull (FDH) technology in general requires solving nonlinear mixed integer programs. Cherchye et al. (J Product Anal 15(3):201–215, 2001) provide an enumeration algorithm for the FDH directional distance function in case of a variable returns to scale technology. In this contribution, we provide fast enumeration algorithms for the FDH directional distance functions under constant, nonincreasing, and nondecreasing returns to scale assumptions. Consequently, enumeration algorithms are now available for all commonly used returns to scale assumptions.

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.

Fig. 1
Fig. 2

Notes

  1. One often uses the moniker Data Envelopment Analysis (DEA) when imposing convexity on technology.

  2. These same authors also innovate methodologically by adding lower and upper bound restrictions to scaling in these extended FDH models.

  3. Note that the directional distance function is more general than the graph-oriented efficiency measure mentioned above. First, the direction vector can take any values. Second, while the directional distance function is dual to the profit function, a graph-oriented (or hyperbolic) efficiency measure is only dual to the return to the dollar function which measures profitability (see Färe et al. 2002).

  4. Sometimes the motivation to maintain convexity is just analytical convenience (see, e.g., Hackman 2008, p. 2). This is an argument that can hardly be taken seriously.

  5. Other measures (e.g., plant capacity utilization measures) can easily be derived from the choices mentioned here.

  6. Empirical data sets of this size are rarely publicly available [e.g., in the Journal of Applied Econometrics Data Archive (http://qed.econ.queensu.ca/jae/)] for the purpose of our illustration.

References

  • Afriat, S. (1972). Efficiency estimation of production functions. International Economic Review, 13(3), 568–598.

    Article  Google Scholar 

  • Agrell, P., & Tind, J. (2001). A dual approach to nonconvex frontier models. Journal of Productivity Analysis, 16(2), 129–147.

    Article  Google Scholar 

  • Akçay, A., Ertek, G., & Büyüközkan, G. (2012). Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework. Expert Systems with Applications, 39(9), 7763–7775.

    Article  Google Scholar 

  • Alam, I., & Sickles, R. (2000). Time series analysis of deregulatory dynamics and technical efficiency: The case of the US Airline Industry. International Economic Review, 41(1), 203–218.

    Article  Google Scholar 

  • Balaguer-Coll, M., Prior, D., & Tortosa-Ausina, E. (2007). On the determinants of local government performance: A two-stage nonparametric approach. European Economic Review, 51(2), 425–451.

    Article  Google Scholar 

  • Barr, R. (2004). DEA software tools and technology: A state-of-the-art survey. In W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 539–566). Boston: Kluwer.

    Chapter  Google Scholar 

  • Briec, W., & Kerstens, K. (2006). Input, output and graph technical efficiency measures on non-convex FDH models with various scaling laws: An integrated approach based upon implicit enumeration algorithms. TOP, 14(1), 135–166.

    Article  Google Scholar 

  • Briec, W., Kerstens, K., & Vanden Eeckaut, P. (2004). Non-convex technologies and cost functions: Definitions, duality and nonparametric tests of convexity. Journal of Economics, 81(2), 155–192.

    Article  Google Scholar 

  • Cesaroni, G. (2011). A complete FDH efficiency analysis of a diffused production network: The case of the Italian Driver and Vehicle Agency. International Transactions in Operational Research, 18(2), 205–229.

    Article  Google Scholar 

  • Cesaroni, G., Kerstens, K., & Van de Woestyne, I. (2017). A new input-oriented plant capacity notion: Definition and empirical comparison. Pacific Economic Review, 22(4), 720–739.

    Article  Google Scholar 

  • Chambers, R., Chung, Y., & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98(2), 351–364.

    Article  Google Scholar 

  • Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Cherchye, L., Kuosmanen, T., & Post, T. (2001). FDH directional distance functions with an application to European commercial banks. Journal of Productivity Analysis, 15(3), 201–215.

    Article  Google Scholar 

  • Chong, E., & Zak, S. (2001). Introduction to optimization (2nd ed.). New York: Wiley.

    Google Scholar 

  • Cullinane, K., Song, D.-W., & Wang, T. (2005). The Application of mathematical programming approaches to estimating container port production efficiency. Journal of Productivity Analysis, 24(1), 73–92.

    Article  Google Scholar 

  • Cummins, D., & Zi, H. (1998). Comparison of Frontier efficiency methods: An application to the U.S. Life Insurance Industry. Journal of Productivity Analysis, 10(2), 131–152.

    Article  Google Scholar 

  • De Borger, B., & Kerstens, K. (1996). Cost efficiency of Belgian Local Governments: A comparative analysis of FDH, DEA, and econometric approaches. Regional Science and Urban Economics, 26(2), 145–170.

    Article  Google Scholar 

  • De Witte, K., & Marques, R. (2011). Big and beautiful? On non-parametrically measuring scale economies in non-convex technologies. Journal of Productivity Analysis, 35(3), 213–226.

    Article  Google Scholar 

  • Deprins, D., Simar, L., & Tulkens, H. (1984). Measuring labor efficiency in post offices. In M. Marchand, P. Pestieau, & H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurements (pp. 243–268). Amsterdam: North Holland.

    Google Scholar 

  • Destefanis, S., & Sena, V. (2005). Public capital and total factor productivity: New evidence from the Italian regions, 1970–98. Regional Studies, 39(5), 603–617.

    Article  Google Scholar 

  • Diewert, W., & Parkan, C. (1983). Linear programming test of regularity conditions for production functions. In W. Eichhorn, K. Neumann, & R. Shephard (Eds.), Quantitative studies on production and prices (pp. 131–158). Würzburg: Physica-Verlag.

    Chapter  Google Scholar 

  • Dulá, J. (2008). A computational study of DEA with massive data sets. Computers & Operations Research, 35(4), 1191–1203.

    Article  Google Scholar 

  • Eiselt, H., & Sandblom, C.-L. (2007). Linear programming and its applications. Berlin: Springer.

    Google Scholar 

  • Epstein, M., & Henderson, J. (1989). Data envelopment analysis for managerial control and diagnosis. Decision Sciences, 20(1), 90–119.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., & Zaim, O. (2002). Hyperbolic efficiency and return to the dollar. European Journal of Operational Research, 136(3), 671–679.

    Article  Google Scholar 

  • Fried, H., Lovell, C., & Turner, J. (1996). An analysis of the performance of university affiliated credit unions. Computers & Operations Research, 23(4), 375–384.

    Article  Google Scholar 

  • Fried, H., Lovell, C., & Vanden Eeckaut, P. (1993). Evaluating the performance of U.S. credit unions. Journal of Banking & Finance, 17(2–3), 251–265.

    Article  Google Scholar 

  • Green, R. (1996). DIY DEA: Implementing data envelopment analysis in the mathematical programming language AMPL. Omega, 24(4), 489–494.

    Article  Google Scholar 

  • Hackman, S. (2008). Production economics: Integrating the microeconomic and engineering perspectives. Berlin: Springer.

    Google Scholar 

  • Kerstens, K., & Managi, S. (2012). Total factor productivity growth and convergence in the petroleum industry: Empirical analysis testing for convexity. International Journal of Production Economics, 139(1), 196–206.

    Article  Google Scholar 

  • Kerstens, K., & Van de Woestyne, I. (2014a). Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data. European Journal of Operational Research, 233(3), 749–758.

    Article  Google Scholar 

  • Kerstens, K., & Van de Woestyne, I. (2014b). Solution methods for nonconvex free disposal hull models: A review and some critical comments. Asia-Pacific Journal of Operational Research, 31(1), 1450010.

    Article  Google Scholar 

  • Kerstens, K., & Vanden Eeckaut, P. (1999). Estimating returns to scale using nonparametric deterministic technologies: A new method based on goodness-of-fit. European Journal of Operational Research, 113(1), 206–214.

    Article  Google Scholar 

  • Leleu, H. (2006). A linear programming framework for free disposal hull technologies and cost functions: Primal and dual models. European Journal of Operational Research, 168(2), 340–344.

    Article  Google Scholar 

  • Leleu, H. (2009). Mixing DEA and FDH models together. Journal of the Operational Research Society, 60(1), 1730–1737.

    Article  Google Scholar 

  • Luenberger, D. (1992a). Benefit function and duality. Journal of Mathematical Economics, 21(5), 461–481.

    Article  Google Scholar 

  • Luenberger, D. (1992b). New optimality principles for economic efficiency and equilibrium. Journal of Optimization Theory and Applications, 75(2), 221–264.

    Article  Google Scholar 

  • Luenberger, D. (1995). Microeconomic theory. Boston: McGraw-Hill.

    Google Scholar 

  • Mairesse, F., & Vanden Eeckaut, P. (2002). Museum assessment and FDH technology: Towards a global approach. Journal of Cultural Economics, 26(4), 261–286.

    Article  Google Scholar 

  • Mayston, D. (2014). Effectiveness analysis of quality achievements for university departments of economics. Applied Economics, 46(31), 3788–3797.

    Article  Google Scholar 

  • Olesen, O., & Petersen, N. (1996). A presentation of GAMS for DEA. Computers & Operations Research, 23(4), 323–339.

    Article  Google Scholar 

  • Podinovski, V. (2004). On the linearisation of reference technologies for testing returns to scale in FDH models. European Journal of Operational Research, 152(3), 800–802.

    Article  Google Scholar 

  • Sueyoshi, T. (1992). Measuring technical, allocative and overall efficiencies using a DEA algorithm. Journal of the Operational Research Society, 43(2), 141–155.

    Article  Google Scholar 

  • Tulkens, H. (1993). On FDH efficiency analysis: Some methodological issues and applications to retail banking, courts, and urban transit. Journal of Productivity Analysis, 4(1–2), 183–210.

    Article  Google Scholar 

  • Walden, J., & Tomberlin, D. (2010). Estimating fishing vessel capacity: A comparison of nonparametric frontier approaches. Marine Resource Economics, 25(1), 23–36.

    Article  Google Scholar 

  • Zhu, D. (2010). A hybrid approach for efficient ensembles. Decision Support Systems, 48(3), 480–487.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristiaan Kerstens.

Additional information

We acknowledge helpful comments of three most constructive referees. The usual disclaimer applies.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (py 2 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kerstens, K., Van de Woestyne, I. Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions. Ann Oper Res 271, 1067–1078 (2018). https://doi.org/10.1007/s10479-018-2791-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-2791-5

Keywords

Navigation