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Envelopment DEA Models

In: Quantitative Models for Performance Evaluation and Benchmarking

Author

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  • Joe Zhu

    (Worcester Polytechnic Institute)

Abstract

This chapter presents some basic DEA models that are used to determine the best-practice frontier characterized by (Sect. 1.1) in Chap. 1. These models are called envelopment models, because the identified best-practice frontier envelops all the observations (DMUs). The shapes of best-practice (or efficient) frontiers obtained from these models can be associated with the concept of Returns-to-Scale (RTS) which will be discussed in details in Chap. 13. This is because the best-practice (or efficient) frontiers can be viewed as exhibiting of various types of RTS. However, if the inputs and outputs are not related to a “production function”, RTS concept cannot be applied. Under such cases, RTS is merely used to refer to different shapes of frontiers.

Suggested Citation

  • Joe Zhu, 2014. "Envelopment DEA Models," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 2, pages 11-48, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-06647-9_2
    DOI: 10.1007/978-3-319-06647-9_2
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    Cited by:

    1. Christina Bampatsou & George Halkos & Olga-Helen Astara, 2022. "Composite indicators in evaluating tourism performance and seasonality," Operational Research, Springer, vol. 22(3), pages 2373-2396, July.
    2. 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.
    3. Bampatsou, Christina & Halkos, George, 2019. "Economic growth, efficiency and environmental elasticity for the G7 countries," Energy Policy, Elsevier, vol. 130(C), pages 355-360.
    4. 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.
    5. 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.
    6. 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.
    7. Saeed Assani & Jianlin Jiang & Ahmad Assani & Feng Yang, 2019. "Estimating and decomposing most productive scale size in parallel DEA networks with shared inputs: A case of China's Five-Year Plans," Papers 1910.03421, arXiv.org, revised Oct 2019.
    8. Shulei Cheng & Wei Fan & Jianlin Wang, 2022. "Investigating the humanitarian labor efficiency of China: a factor-specific model," Annals of Operations Research, Springer, vol. 319(1), pages 439-461, December.

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