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Scale efficiency in two-stage network DEA

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  • Kun Chen
  • Joe Zhu

Abstract

Network data envelopment analysis (DEA) considers internal structures of decision-making units. Unlike the standard DEA, network DEA imposes hurdles for measuring scale efficiency due to the fact that (i) overall efficiency aggregated by the stage or divisional technical efficiencies is highly non-linear and only solvable in a heuristic manner, or (ii) the overall efficiency which concerns exclusively inputs and outputs of a system is difficult to be decomposed into divisional efficiencies. In this paper, we establish a mathematical transformation to convert the corresponding non-linear programming problem into second order cone programming programme. The transformation is shown to be versatile in dealing with both constant returns to scale and variable returns to scale models under the two-stage network DEA. Meanwhile, our numerical results reveal that overall scale efficiency in two-stage network DEA is consistent with scale efficiency in conventional DEA.

Suggested Citation

  • Kun Chen & Joe Zhu, 2019. "Scale efficiency in two-stage network DEA," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(1), pages 101-110, January.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:1:p:101-110
    DOI: 10.1080/01605682.2017.1421850
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    Cited by:

    1. Lee, Hsuan-Shih, 2021. "Efficiency decomposition of the network DEA in variable returns to scale: An additive dissection in losses," Omega, Elsevier, vol. 100(C).
    2. Alireza Moradi & Saber Saati & Mehrzad Navabakhsh, 2023. "Genetic algorithms for optimizing two-stage DEA by considering unequal intermediate weights," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1202-1217, September.
    3. Wen-Min Lu & Qian Long Kweh & Kai-Chu Yang, 2022. "Multiplicative efficiency aggregation to evaluate Taiwanese local auditing institutions performance," Annals of Operations Research, Springer, vol. 315(2), pages 1243-1262, August.

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