Skip to main content
Log in

The uncertain two-stage network DEA models

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The two-stage network DEA models based on the framework that the efficiency of the whole stage is equal to the product of the efficiencies of two sub-stages can not only turn the ‘black box’ into the ‘glass box’ to identify the root causes of the inefficiency of the network system, but also consider the relationship between the two sub-stages within the whole stage. Nowadays, the two-stage network DEA models have been widely applied in the field of economy and management, such as green supply chain and reverse supply chain. Due to the novelty of evaluation indexes, these emerging research objects with network structure, such as green supply chain, involve not only traditional evaluation indexes such as cost and time, but also some novel evaluation indexes such as customer satisfaction and flexibility. However, these new evaluation indexes are difficult to quantify accurately, which will lead to the failure of the traditional two-stage network DEA models. Therefore, this paper attempts to extend the traditional two-stage network DEA models to the uncertain two-stage network DEA models with the application of uncertainty theory. In the new models, inputs, intermediates and outputs are considered to be uncertain variables to deal with the problem of inaccurate data. Finally, a numerical example of the uncertain two-stage network DEA models will be presented for illustration.

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.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Banker RD (1993) Maximum likelihood, consistency and DEA: statistical foundations. Manag Sci 39(10):1265–1273

    Article  MATH  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  MathSciNet  MATH  Google Scholar 

  • Chen C (2009) A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks. Eur J Oper Res 194(3):687–699

    Article  MathSciNet  MATH  Google Scholar 

  • Chen Y, Zhu J (2004) Measuring information technology’s indirect impact on firm performance. Inf Technol Manag J 5(1–2):9–22

    Article  Google Scholar 

  • Cook WD, Zhu J (2010) Network DEA: additive efficiency decomposition. Eur J Oper Res 207(2):1122–1129

    Article  MATH  Google Scholar 

  • Cook WD, Tone K, Zhu J (2014) Data envelopment analysis: prior to choosing a model. Omega 44:1–4

    Article  Google Scholar 

  • Dyckhoff H, Allen K (2001) Measuring ecological efficiency with data envelopment analysis (DEA). Eur J Oper Res 132(2):312–325

    Article  MATH  Google Scholar 

  • Esmaeilzadeh A, Matin RK (2019) Multi-period efficiency measurement of network production systems. Measurement 134:835–844

    Article  Google Scholar 

  • Fa̋re R, Grosskopf S (2000) Network DEA. Soc Econ Plan Sci 34(1):35–49

    Article  Google Scholar 

  • Hatami-Marbini A, Saati S (2018) Efficiency evaluation in two-stage data envelopment analysis under a fuzzy environment: a common-weights approach. Appl Soft Comput 72:156–165

    Article  Google Scholar 

  • Jiang B, Lio W, Li X (2019) An uncertain DEA model for scale efficiency evaluation. IEEE Trans Fuzzy Syst 27(8):1616–1624

    Article  Google Scholar 

  • Jiang B, Zou Z, Lio W, Li J (2020) The uncertain DEA models for specific scale efficiency identification. J Intell Fuzzy Syst 38:3403–3417

    Article  Google Scholar 

  • Kao C (2011) Efficiencies of two-stage systems with fuzzy data. Fuzzy Sets Syst 176(1):20–35

    Article  MathSciNet  MATH  Google Scholar 

  • Kao C (2014) Network data envelopment analysis: a review. Eur J Oper Res 239(1):1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Kao C (2016) Efficiency decomposition and aggregation in network data envelopment analysis. Eur J Oper Res 255(3):778–786

    Article  MathSciNet  MATH  Google Scholar 

  • Kao C, Hwang S (2008) 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

    Article  MATH  Google Scholar 

  • Lio W, Liu B (2018) Uncertain data envelopment analysis with imprecisely observed inputs and outputs. Fuzzy Optim Decis Mak 17:357–373

    Article  MathSciNet  MATH  Google Scholar 

  • Liu B (2007) Uncertainty theory, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  • Liu B (2009) Theory and practice of uncertain programming, 2nd edn. Springer, Berlin

    Book  MATH  Google Scholar 

  • Liu B (2009) Some research problems in uncertainty theory. J Uncertain Syst 3(1):3–10

    Google Scholar 

  • Liu B (2010) Uncertainty theory: a branch of mathematics for modeling human uncertainty. Springer, Berlin

    Book  Google Scholar 

  • Liu B (2012) Why is there a need for uncertainty theory. J Uncertain Syst 6:3–10

    Google Scholar 

  • Liu B, Chen X (2015) Uncertain multiobjective programming and uncertain goal programming. J Uncertain Anal Appl 3:10

    Article  Google Scholar 

  • Liu Y, Ha M (2010) Expected value of function of uncertain variables. J Uncertain Syst 4(3):181–186

    Google Scholar 

  • Liu B, Yao K (2015) Uncertain multilevel programming: algorithm and application. Comput Ind Eng 89:235–240

    Article  Google Scholar 

  • Olesen OB, Petersen NC (2016) Stochastic data envelopment analysis-a review. Eur J Oper Res 251(1):2–21

    Article  MathSciNet  MATH  Google Scholar 

  • Sadjadi SJ, Omrani H (2008) Data envelopment analysis with uncertain data: an application for iranian electricity distribution companies. Energy Policy 36(11):4247–4254

    Article  Google Scholar 

  • Seiford LM, Zhu J (1999) Profitability and marketability of the top 55 US commercial banks. Manag Sci 45(9):1270–1288

    Article  Google Scholar 

  • Sengupta JK (1982) Efficiency measurement in stochastic input-output systems. Int J Syst Sci 13(3):273–287

    Article  MathSciNet  MATH  Google Scholar 

  • Sexton TR, Lewis HF (2003) Two-stage DEA: an application to major league baseball. J Prod Anal 19(2–3):227–249

    Article  Google Scholar 

  • Sueyoshi T (2000) Stochastic DEA for restructure strategy: an application to a Japanese petroleum company. Omega 28(4):385–398

    Article  Google Scholar 

  • Tavana M (2018) Efficiency decomposition and measurement in two-stage fuzzy DEA models using a bargaining game approach. Comput Ind Eng 118:394–408

    Article  Google Scholar 

  • Wanke P, Kalam Azad A, Emrouznejad A (2018) Efficiency in BRICS banking under data vagueness: a two-stage fuzzy approach. Glob Financ J 35:58–71

    Article  Google Scholar 

  • Wen M, Guo L, Kang R, Yang Y (2014) Data envelopment analysis with uncertain inputs and outputs. J Appl Math 2:1–7

    MATH  Google Scholar 

Download references

Funding

This work was supported by National Natural Science Foundation of China Grant No. 61873329 and supported by the Fundamental Research Funds for the Central Universities No. 201713011.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian Li.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies with human participants performed by the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, B., Chen, H., Li, J. et al. The uncertain two-stage network DEA models. Soft Comput 25, 421–429 (2021). https://doi.org/10.1007/s00500-020-05157-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05157-3

Keywords

Navigation