Skip to main content
SpringerLink
Log in

A joint measurement of efficiency and effectiveness using network data envelopment analysis approach in the presence of shared input

  • Theoretical Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

This paper proposes a novel network data envelopment analysis (NDEA) approach to measure both technical efficiency and service effectiveness of railway transportation services. In particular, railway transportation systems consume varying amounts of shared inputs to produce outputs. The proposed NDEA model represents both the non-storable feature of transportation service and production technologies in a unified framework in the presence of shared inputs. Hence, the proposed model not only measures the overall efficiency of the railways, but also estimates passenger and freight technical efficiency, service effectiveness, and technical effectiveness, simultaneously. A case study validates the proposed model.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. André, F.J., Herrero, I., Riesgo, L.: A modified DEA model to estimate the importance of objectives with an application to agricultural economics. Omega 38(5), 371–382 (2010)

    Article  Google Scholar 

  2. Avkiran, N., McCrystal, A.: Sensitivity analysis of network DEA: NSBM versus NRAM. Appl. Math. Comput. 218(22), 11226–11239 (2012)

    Article  Google Scholar 

  3. Avkiran, N.K., Morita, H.: Benchmarking firm performance from a multiple-stakeholder perspective with an application to Chinese banking. Omega 38(6), 501–508 (2010)

    Article  Google Scholar 

  4. Azadi, M., Farzipoor Saen, R.: Developing a new chance-constrained DEA model for suppliers selection in the presence of undesirable outputs. Int. J. Oper. Res 13(1), 44–66 (2012)

    Article  Google Scholar 

  5. Azadi, M., Farzipoor Saen, R., Tavana, M.: Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data. Int. J. Ind. Syst. Eng. 10(2), 167–196 (2012)

    Google Scholar 

  6. Banker, R., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30(9), 1078–1092 (1984)

    Article  Google Scholar 

  7. Chang, Y.-T., Zhang, N., Danao, D., Zhang, N.: Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58, 277–283 (2013)

    Article  Google Scholar 

  8. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 23(1), 429–444 (1978)

    Article  Google Scholar 

  9. Chen, Y., Cook, W.D., Li, N., Zhu, J.: Additive efficiency decomposition in two-stage DEA. Eur. J. Oper. Res. 196(3), 1170–1176 (2009)

    Article  Google Scholar 

  10. Chiou, Y.C., Lan, L.W., Yen, B.T.H.: A joint measurement of efficiency and effectiveness for non-storable commodities: intenerated data envelopment analysis approaches. Eur. J. Oper. Res. 201(2), 477–489 (2010)

    Article  Google Scholar 

  11. Cook, D., Zhu, J., Bi, G., Yang, F.: Network DEA: additive efficiency decomposition. Eur. J. Oper. Res. 207(2), 1122–1129 (2010)

    Article  Google Scholar 

  12. De Nicola, A., Gitto, S., Mancuso, P.: Uncover the predictive structure of healthcare efficiency applying a bootstrapped data envelopment analysis. Expert Syst. Appl. 39(12), 10495–10499 (2012)

    Article  Google Scholar 

  13. Farzipoor Saen, R.: Supplier selection by the pair of nondiscretionaryfactors-imprecise data envelopment analysis models. J. Oper. Res. Soc. 60(11), 1575–1582 (2009)

    Article  Google Scholar 

  14. Fielding, G.J.: Managing Public Transit Strategically. Jossey-Bass, San Francisco (1987)

    Google Scholar 

  15. Fukuyama, H., Mirdehghan, S.M.: Identifying the efficiency status in network DEA. Eur. J. Oper. Res. 220(1), 85–92 (2012)

    Article  Google Scholar 

  16. Graham, D.J.: Productivity and efficiency in urban railways: parametric and non-parametric estimates. Transp. Res. E Logist. Transp. Rev. 44(1), 84–99 (2008)

    Article  Google Scholar 

  17. Heidari, M.D., Omid, M., Mohammadi, A.: Measuring productive efficiency of horticultural greenhouses in Iran: a data envelopment analysis approach. Expert Syst. Appl. 39(1), 1040–1045 (2012)

    Article  Google Scholar 

  18. Hirschhausen, C.V., Cullmann, A.: A nonparametric efficiency analysis of German public transport companies. Transp. Res. E Logist. Transp. Rev. 46(3), 436–445 (2010)

    Article  Google Scholar 

  19. Jitsuzumi, T., Nakamura, A.: Causes of inefficiency in Japanese railways: application of DEA for managers and policymakers. Socio Econ. Plan. Sci. 44(3), 161–173 (2010)

    Article  Google Scholar 

  20. Kao, C., Hwang, S.N.: Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur. J. Oper. Res. 185(1), 418–429 (2008)

    Article  Google Scholar 

  21. Khodakarami, M., Shabani, A., Farzipoor Saen, R.: Concurrent estimation of efficiency and effectiveness and returns to scale. Int. J. Syst. Sci. (in press)

  22. Liang, L., Cook, W.D., Zhu, J.: DEA model for two-stage processes: game approach and efficiency decomposition. Nav. Res. Logist. 55(1), 643–653 (2008)

    Article  Google Scholar 

  23. Lin, E.T.J.: Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis: a comment. Transp. Res. E Logist. Transp. Rev. 44(5), 894–899 (2008)

    Article  Google Scholar 

  24. Lin, R.C., Sir, M.Y., Pasupathy, K.S.: Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: specific application to determining optimal resource levels in surgical services. Omega 41(5), 881–892 (2013)

    Article  Google Scholar 

  25. Liu, J.S., Lu, L.Y.Y., Lu, W.M., Lin, B.J.Y.: A survey of DEA applications. Omega 41(5), 893–902 (2013)

    Article  Google Scholar 

  26. Meng, W., Zhang, D., Qi, L., Liu, W.: Two-level DEA approaches in research evaluation. Omega 36(6), 950–957 (2008)

    Article  Google Scholar 

  27. Montoneri, B., Lee, C.C., Lin, T.T., Huang, S.L.: A learning performance evaluation with benchmarking concept for English writing courses. Expert Syst. Appl. 38(12), 14542–14549 (2011)

    Article  Google Scholar 

  28. Puri, J., Yadav, S.P.: A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector. Expert Syst. Appl. 40(5), 1437–1450 (2013)

    Article  Google Scholar 

  29. Raab, R., Lichty, R.W.: Identifying sub-areas that comprise a greater metropolitan area: the criterion of county relative efficiency. J. Reg. Sci. 42(3), 579–594 (2002)

    Article  Google Scholar 

  30. Samoilenko, S., Osei-Bryson, K.-M.: Using data envelopment analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: design and implementation of a decision support system. Omega 41(1), 131–142 (2013)

    Article  Google Scholar 

  31. Tavassoli, M., Faramarzi, G.R., Farzipoor Saen, R.: A joint measurement of efficiency and effectiveness for the best supplier selection using integrated data envelopment analysis approach. Int. J. Math. Oper. Res. 6(1), 70–83 (2014)

    Article  Google Scholar 

  32. Tavassoli, M., Faramarzi, G.R., Farzipoor Saen, R.: Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input. J. Air Transp. Manag. 34, 146–153 (2014)

    Article  Google Scholar 

  33. Yu, M.M.: Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world’s railways through NDEA analysis. Transp. Res. A 42(10), 1283–1294 (2008)

    Google Scholar 

  34. Yu, M.M., Lin, E.T.J.: Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega 36(6), 1005–1017 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to thank four anonymous Reviewers for insightful comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran

    Mohammad Tavassoli

  2. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran

    Gholam Reza Faramarzi

  3. Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, P. O. Box: 31485-313, Iran

    Reza Farzipoor Saen

Authors
  1. Mohammad Tavassoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gholam Reza Faramarzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Reza Farzipoor Saen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Farzipoor Saen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tavassoli, M., Faramarzi, G.R. & Saen, R.F. A joint measurement of efficiency and effectiveness using network data envelopment analysis approach in the presence of shared input. OPSEARCH 52, 490–504 (2015). https://doi.org/10.1007/s12597-014-0188-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-014-0188-z

Keywords

Search

Navigation