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Using DEA to Identify and Manage Congestion

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  • Patrick Brockett
  • William Cooper
  • Honghui Deng
  • Linda Golden
  • T. Ruefli

Abstract

This paper deals with identifying and managing congestion. For this purpose, DEA (Data Envelopment Analysis) is used to identify congestion when the data show it to be present, estimate its amounts, and separate it from other forms of inefficiency. DEA is also used to identify where improvements may be made in the management of congestion and to estimate input decreases and output increases that may be made after managerial inefficiencies in managing congestion are eliminated. The treatment here differs from the usual approaches that are restricted to identifying sources and amounts of technical inefficiency and congestion to be eliminated. The focus is directed rather to efficiency of performances in the presence of inefficiencies imposed by, say, labor contracts or government regulations and policies. Other developments include a use of rates of substitution formulated in terms of slack variables that help to avoid instabilities associated with the very small values that are often encountered in the use of dual variables to determine the rates of substitution. These rates of substitution are intended for use in guiding allocations (or reallocations) of inputs between different plants (or other entities) in ways that can further improve performance without reducing the congesting inputs that are to be employed. Hence modifications are needed to extend the usual restrictions to movements on the efficiency frontier so that frontiers associated with congestion and other inefficiencies can be dealt with. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Patrick Brockett & William Cooper & Honghui Deng & Linda Golden & T. Ruefli, 2004. "Using DEA to Identify and Manage Congestion," Journal of Productivity Analysis, Springer, vol. 22(3), pages 207-226, November.
  • Handle: RePEc:kap:jproda:v:22:y:2004:i:3:p:207-226
    DOI: 10.1007/s11123-004-7574-0
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    References listed on IDEAS

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    Cited by:

    1. Wei, Quanling & Yan, Hong, 2009. "Weak congestion in output additive data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 43(1), pages 40-54, March.
    2. Yang, Zhuofan & Shi, Yong & Yan, Hong, 2017. "Analysis on pure e-commerce congestion effect, productivity effect and profitability in China," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 35-49.
    3. Mehdiloozad, Mahmood & Zhu, Joe & Sahoo, Biresh K., 2018. "Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data," European Journal of Operational Research, Elsevier, vol. 265(2), pages 644-654.
    4. Herimalala, Rahobisoa & Gaussens, Olivier, 2012. "X-Efficiency of Innovation Processes: Concept and Evaluation based on Data Envelopment Analysis," MPRA Paper 41887, University Library of Munich, Germany.
    5. Flegg, A.T. & Allen, D.O., 2009. "Congestion in the Chinese automobile and textile industries revisited," Socio-Economic Planning Sciences, Elsevier, vol. 43(3), pages 177-191, September.
    6. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    7. Jun Wang & Yong Zha, 2014. "Distinguishing Technical Inefficiency from Desirable and Undesirable Congestion with an Application to Regional Industries in China," Sustainability, MDPI, vol. 6(12), pages 1-19, December.
    8. Fang, Lei, 2015. "Congestion measurement in nonparametric analysis under the weakly disposable technology," European Journal of Operational Research, Elsevier, vol. 245(1), pages 203-208.
    9. Cooper, W.W. & Huang, Zhimin & Li, Susan X. & Parker, Barnett R. & Pastor, Jesus T., 2007. "Efficiency aggregation with enhanced Russell measures in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 41(1), pages 1-21, March.
    10. Zhang, Yue-Jun & Liu, Jing-Yue & Su, Bin, 2020. "Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels," Energy Economics, Elsevier, vol. 86(C).
    11. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2009. "DEA congestion and returns to scale under an occurrence of multiple optimal projections," European Journal of Operational Research, Elsevier, vol. 194(2), pages 592-607, April.

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