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Congestion measurement and elimination under the framework of data envelopment analysis

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  • Kao, Chiang

Abstract

Congestion is a wasteful stage of the production process where outputs are reduced due to excessive amount of inputs. This paper investigates three types of models prevailing in the data envelopment analysis literature, i.e., the Färe-family model, the Cooper-family model, and the Wei-Tone model, for measuring the congestion effect. The Färe-family model is able to decompose technical efficiency into the product of pure technical efficiency and congestion effect, yet fails to detect congestion in some situations. The Cooper-family model successfully detects congestion in all situations, yet is unable to differentiate pure technical efficiency with the congestion effect. The Wei-Tone model does not have the aforementioned deficiencies, yet is unable to identify the excessive amounts of each input. To illustrate how to eliminate congestion in reality, the Wei-Tone model is utilized to measure the congestion effect of Taiwan forests. There are three forest districts found to have different extents of congestion. The theoretical method of deleting excessive inputs to improve efficiency is actually impractical in this case. By means of reorganization, this paper finds that the congestion effects of two districts are eliminated and the other is alleviated.

Suggested Citation

  • Kao, Chiang, 2010. "Congestion measurement and elimination under the framework of data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 123(2), pages 257-265, February.
  • Handle: RePEc:eee:proeco:v:123:y:2010:i:2:p:257-265
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    References listed on IDEAS

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    Cited by:

    1. Holden, R. & Xu, B. & Greening, P. & Piecyk, M. & Dadhich, P., 2016. "Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 105-119.
    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. F. Wu & P. Zhou & D. Zhou, 2015. "Measuring Energy Congestion in Chinese Industrial Sectors: A Slacks-Based DEA Approach," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 479-494, October.
    4. repec:eee:ejores:v:265:y:2018:i:2:p:644-654 is not listed on IDEAS
    5. 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.
    6. Yang, Guo-liang & Rousseau, Ronald & Yang, Li-ying & Liu, Wen-bin, 2014. "A study on directional returns to scale," Journal of Informetrics, Elsevier, vol. 8(3), pages 628-641.
    7. Wang, Ying-Ming & Chin, Kwai-Sang, 2010. "Some alternative models for DEA cross-efficiency evaluation," International Journal of Production Economics, Elsevier, vol. 128(1), pages 332-338, November.
    8. repec:eee:enepol:v:110:y:2017:i:c:p:710-718 is not listed on IDEAS
    9. Wu, Jie & An, Qingxian & Xiong, Beibei & Chen, Ya, 2013. "Congestion measurement for regional industries in China: A data envelopment analysis approach with undesirable outputs," Energy Policy, Elsevier, vol. 57(C), pages 7-13.
    10. Jun Wang & Yong Zha, 2014. "Distinguishing Technical Inefficiency from Desirable and Undesirable Congestion with an Application to Regional Industries in China," Sustainability, MDPI, Open Access Journal, vol. 6(12), pages 1-19, December.

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