Skip to main content
SpringerLink
Log in

The network data envelopment analysis models for non-homogenous decision making units based on the sun network structure

  • Original Paper
  • Published:
Central European Journal of Operations Research Aims and scope Submit manuscript

Abstract

This paper seeks to propose a network data envelopment analysis (DEA) framework for analysis of heterogeneous systems. The paper introduces the dummy connector so that every network structure can be transformed into the sun network structure. In his case, the dummy connector allows for heterogeneity of the decision making units (DMUs) in terms of their inner structure. Based on the sun network structure, the static and dynamic network DEA models are established. Thus, DMUs with different structures can be evaluated according to the static and dynamic network DEA models. The efficiency of each sub-unit, each period and each sub-unit in each period can also be obtained. Two simulated examples are presented using the static and dynamic DEA models.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Barat M, Tohidi G, Sanei M (2018) DEA for nonhomogeneous mixed networks. Asia Pac Manag Rev. https://doi.org/10.1016/j.apmrv.2018.02.003

    Article  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  Google Scholar 

  • Charnes A, Cooper WW, Golany B, Halek R, Klopp G, Schmitz E, Thomas D (1986) Two-phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: tradeoffs between joint services and army advertising. Technical Report, Research Report CCS, 532

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

    Article  Google Scholar 

  • Chen K, Zhu J (2018) Scale efficiency in two-stage network DEA. J Oper Res Soc. https://doi.org/10.1080/01605682.2017.1421850

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chen Y, Cook WD, Zhu J (2010) Deriving the DEA frontier for the two-stage process. Eur J Oper Res 202(1):138–142

    Article  Google Scholar 

  • Chen CL, Zhu J, Yu JY, Noori H (2012) A new methodology for evaluating sustainable product design performance with two-stage network data envelopment analysis. Eur J Oper Res 221(2):348–359

    Article  Google Scholar 

  • Chen Y, Cook WD, Kao C, Zhu J (2013) Network DEA pitfall: divisional efficiency and frontier projection under general network structures. Eur J Oper Res 226(3):507–515

    Article  Google Scholar 

  • Cook WD, Seiford LM (2011) Data envelopment analysis (DEA)—thirty years on. Eur J Oper Res 212(2):411–416

    Article  Google Scholar 

  • Cook WD, Liang L, Zhu J (2010) Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38(6):423–430

    Article  Google Scholar 

  • Du J, Liang L, Chen Y, Cook WD, Zhu J (2011) A bargaining game model for measuring performance of two-stage network structures. Eur J Oper Res 210(2):390–397

    Article  Google Scholar 

  • Emrouznejad A, Parker BR, Tavares G (2008) Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholar literature in DEA. Socio Econ Plan Sci 42(3):151–157

    Article  Google Scholar 

  • Fang ST, Ji X, Ji XH, Wu J (2018) Sustainable urbanization performance evaluation and benchmarking: an efficiency perspective. Manag Env Qual 29(2):240–254. https://doi.org/10.1108/MEQ-07-2017-0063

    Article  Google Scholar 

  • Färe R, Grosskopf S, Brannlund R (1996) Intertemporal production frontiers: with dynamic DEA. Kluwer Academic, Boston

    Book  Google Scholar 

  • Färe R, Grabowski R, Grosskopf S, Kraft S (1997) Efficiency of a fixed but allocatable input: a non-parametric approach. Econ Lett 56(2):187–193

    Article  Google Scholar 

  • Guo C, Shureshjani RA, Foroughi AA, Zhu J (2017) Decomposition weights and overall efficiency in two-stage additive network DEA. Eur J Oper Res 257(3):896–906

    Article  Google Scholar 

  • Jablonsky J (2016) Efficiency analysis in multi-period systems: an application to performance evaluation in Czech higher education. Cent Eur J Oper Res 24:283–296. https://doi.org/10.1007/s10100-015-0401-z

    Article  Google Scholar 

  • Jablonsky J (2018) Ranking of countries in sporting events using two-stage data envelopment analysis models: a case of Summer Olympic Games 2016. Cent Eur J Oper Res. https://doi.org/10.1007/s10100-018-0537-8

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kapelko M (2018) Measuring inefficiency for specific inputs using data envelopment analysis: evidence from construction industry in Spain and Portugal. Cent Eur J Oper Res 26:43–66. https://doi.org/10.1007/s10100-017-0473-z

    Article  Google Scholar 

  • Keshavarz Ghorabaee M, Amiri M, Olfat L, Khatami Firouzabadi SMA (2017) Designing a multi-product multi-period supply chain network with reverse logistics and multiple objectives under uncertainty. Technol Econ Dev Eco 23(3):520–548

    Article  Google Scholar 

  • Li H, Chen C, Cook WD, Zhang J, Zhu J (2018) Two-stage network DEA: who is the leader? Omega 74:15–19

    Article  Google Scholar 

  • Lim SM, Zhu J (2013) Integrated data envelopment analysis: global vs. local optimum. Eur J Oper Res 229(1):276–278

    Article  Google Scholar 

  • Lv KJ, Wang D, Cheng Y (2017) Measuring the dynamic performances of innovation production process from the carry-over perspective: an empirical study of China’s high-tech industry. Transform Bus Econ 16(3C):345–361

    Google Scholar 

  • Mahdiloo M, Toloo M, Duong TT, Farzipoor Saen R, Tatham P (2018) Integrated data envelopment analysis: linear vs. nonlinear model. Eur J Oper Res 268:255–267

    Article  Google Scholar 

  • Park S, Park KT (2009) Measurement of multiperiod aggregative efficiency. Eur J Oper Res 193(2):567–580

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Seiford LM, Zhu J (1999b) An investigation of returns to scale in data envelopment analysis. Omega 27:1–11

    Article  Google Scholar 

  • Song ML, Peng L, Wang JL, Zhao JJ (2018) Environmental efficiency and economic growth of China: a Ray slack-based model analysis. Eur J Oper Res 269(1):51–63

    Article  Google Scholar 

  • Toloo M, Emrouznejad A, Moreno P (2015) A linear relational DEA model to evaluate two-stage processes with shared inputs. Comput Appl Math 36(1):45–61

    Article  Google Scholar 

  • Toloo M, Nalchigar S, Sohrabi B (2018) Selecting most efficient information system projects in presence of user subjective opinions: a DEA approach. Cent Eur J Oper Res. https://doi.org/10.1007/s10100-018-0549-4

    Article  Google Scholar 

  • Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509

    Article  Google Scholar 

  • Tone K, Tsutsui M (2009) Network DEA: a slacks-based measure approach. Eur J Oper Res 197(1):243–252

    Article  Google Scholar 

  • Tran CDT, Villano RA (2018) Measuring efficiency of Vietnamese public colleges: an application of the DEA-based dynamic network approach. Int Trans Oper Res 25(2):683–703

    Article  Google Scholar 

  • Villa G, Lozano S (2018) Dynamic network DEA approach to basketball games efficiency. J Oper Res Soc. https://doi.org/10.1080/01605682.2017.1409158

    Article  Google Scholar 

  • Wei QL, Chang TS (2011) Optimal system design series-network DEA models. J Oper Res Soc 62(9):1109–1119

    Article  Google Scholar 

  • Yin CB (2017) Environmental efficiency and its determinants in the development of China’s western regions in 2000–2014. Chin J Popul Resour Environ 15(2):157–166. https://doi.org/10.1080/10042857.2017.1327687

    Article  Google Scholar 

  • Zhu J (2000) Multi-factor performance measure model with an application to fortune 500 companies. Eur J Oper Res 123(1):105–124

    Article  Google Scholar 

Download references

Acknowledgements

This research is funded by the European Social Fund according to the activity ‘Improvement of researchers’ qualification by implementing world-class R&D projects’ of Measure No. 09.3.3-LMT-K-712. This research was supported by the 111 Project (No. B18021).

Author information

Authors and Affiliations

  1. School of Economics and Management, North China Electric Power University, Beijing, China

    Qingyou Yan, Fei Zhao & Xu Wang

  2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China

    Guoliang Yang

  3. Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania

    Tomas Baležentis & Dalia Streimikiene

Authors
  1. Qingyou Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guoliang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tomas Baležentis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dalia Streimikiene
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomas Baležentis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Q., Zhao, F., Wang, X. et al. The network data envelopment analysis models for non-homogenous decision making units based on the sun network structure. Cent Eur J Oper Res 27, 1221–1244 (2019). https://doi.org/10.1007/s10100-018-0560-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10100-018-0560-9

Keywords

Search

Navigation