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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.

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  • 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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    3. Xian-tong Ren & Guo-liang Yang, 2024. "Eliminating congestion in China’s papermaking and paper products industry: From both the perspective of increasing and decreasing inputs," Journal of Productivity Analysis, Springer, vol. 61(1), pages 63-82, February.
    4. 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.
    5. 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.
    6. Eshagh Esfandiar & Robabeh Eslami & Mohammad Khoveyni & Alireza Gilani, 2023. "Identifying the closest most productive scale size unit in data envelopment analysis," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 623-660, June.
    7. 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.
    8. Hu, Jin-Li & Chang, Ming-Chung & Tsay, Hui-Wen, 2017. "The congestion total-factor energy efficiency of regions in Taiwan," Energy Policy, Elsevier, vol. 110(C), pages 710-718.
    9. 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.
    10. 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.
    11. Ren, Xian-tong & Fukuyama, Hirofumi & Yang, Guo-liang, 2022. "Eliminating congestion by increasing inputs in R&D activities of Chinese universities," Omega, Elsevier, vol. 110(C).
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