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Network, Shared Flow and Multi-level DEA Models: A Critical Review

In: Data Envelopment Analysis

Author

Listed:
  • Lorenzo Castelli

    (Università degli Studi di Trieste)

  • Raffaele Pesenti

    (Università Ca’ Foscari di Venezia)

Abstract

In the last two decades, complex and detailed DEA models that consider the internal structure of DMUs have been proposed by several authors. This chapter describes the mathematical formulations, along with their main variants, extensions and applications, of three large and popular model families: network (with special emphasis on multi-stage), shared flow (also known as multi-component or multi-activity), and multi-level models. Each family is a different generalization of the same elementary internal structure. This review extends and updates the classification presented in Castelli et al. (Ann Oper Res 173(1):207–235, 2010).

Suggested Citation

  • Lorenzo Castelli & Raffaele Pesenti, 2014. "Network, Shared Flow and Multi-level DEA Models: A Critical Review," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 329-376, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-8068-7_15
    DOI: 10.1007/978-1-4899-8068-7_15
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    Citations

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    Cited by:

    1. Hirofumi Fukuyama & William L. Weber, 2017. "Measuring bank performance with a dynamic network Luenberger indicator," Annals of Operations Research, Springer, vol. 250(1), pages 85-104, March.
    2. Pengyue Wu & Jing Ma & Xiaoyu Guo, 2022. "Efficiency evaluation and influencing factors analysis of fiscal and taxation policies: A method combining DEA-AHP and CD function," Annals of Operations Research, Springer, vol. 309(1), pages 325-345, February.
    3. Abdullah Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    4. Antonio Peyrache & Maria C. A. Silva, 2019. "The Inefficiency of Production Systems and its decomposition," CEPA Working Papers Series WP052019, School of Economics, University of Queensland, Australia.
    5. Pinto, Claudio, 2018. "Performances management when modelling internal structure," MPRA Paper 87923, University Library of Munich, Germany.
    6. Abdullah Korkut Üstün, 2016. "Evaluating İstanbul’s disaster resilience capacity by data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1603-1623, February.
    7. Antonio Peyrache & Maria C. A. Silva, 2022. "Efficiency and Productivity Analysis from a System Perspective: Historical Overview," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 173-230, Springer.
    8. Wey, Wann-Ming & Kang, Chao-Chung & Khan, Haider A., 2020. "Evaluating the effects of environmental factors and a transfer fare discount policy on the performance of an urban metro system," Transport Policy, Elsevier, vol. 97(C), pages 172-185.
    9. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.

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