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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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    1. Cooper, W. W. & Deng, Honghui & Gu, Bisheng & Li, Shanling & Thrall, R. M., 2001. "Using DEA to improve the management of congestion in Chinese industries (1981-1997)," Socio-Economic Planning Sciences, Elsevier, vol. 35(4), pages 227-242, December.
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    4. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. W. Cooper & Dr. Park & Professor Ciurana, 2000. "Marginal Rates and Elasticities of Substitution with Additive Models in DEA," Journal of Productivity Analysis, Springer, vol. 13(2), pages 105-123, March.
    7. Athanassopoulos, Antreas D. & Lambroukos, Nikos & Seiford, Lawrence, 1999. "Data envelopment scenario analysis for setting targets to electricity generating plants," European Journal of Operational Research, Elsevier, vol. 115(3), pages 413-428, June.
    8. Stigler, George J, 1976. "The Xistence of X-Efficiency," American Economic Review, American Economic Association, vol. 66(1), pages 213-216, March.
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    10. Dan Rosen & Claire Schaffnit & Joseph Paradi, 1998. "Marginal Rates and Two-dimensional Level Curves in DEA," Journal of Productivity Analysis, Springer, vol. 9(3), pages 205-232, March.
    11. Cooper, W. W. & Gu, Bisheng & Li, Shanling, 2001. "Note: Alternative treatments of congestion in DEA - a response to the Cherchye, Kuosmanen and Post critique," European Journal of Operational Research, Elsevier, vol. 132(1), pages 81-87, July.
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    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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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