IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v31y2021i1p61-76id1554.html
   My bibliography  Save this article

Detecting congestion in DEA by solving one model

Author

Listed:
  • Maryam Shadab
  • Saber Saati
  • Reza Farzipoor Saen
  • Mohammad Khoveyni
  • Amin Mostafaee

Abstract

The presence of input congestion is one of the key issues that result in lower efficiency and performance in decision-making units (DMUs). So, determination of congestion is of prime importance, and removing it improves the performance of DMUs. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Since the output of inefficient units can be increased by keeping the input constant through projecting on the weak efficiency frontier, it is unnecessary to determine the congested inefficient DMUs. Therefore, in this case, we solely determine congested vertex units. Towards this aim, only one LP model in DEA is proposed and the status of congestion (strong congestion and weak congestion) obtained. In our method, a vertex unit under evaluation is eliminated from the production technology, and then, if there exists an activity that belongs to the production technology with lower inputs and higher outputs compared with the omitted unit, we say vertex unit evidences congestion. One of the features of our model is that by solving only one LP model and with easier and fewer calculations compared to other methods, congested units can be identified. Data set obtained from Japanese chain stores for a period of 27 years is used to demonstrate the applicability of the proposed model and the results are compared with some previous methods.

Suggested Citation

  • Maryam Shadab & Saber Saati & Reza Farzipoor Saen & Mohammad Khoveyni & Amin Mostafaee, 2021. "Detecting congestion in DEA by solving one model," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 61-76.
    • Handle: RePEc:wut:journl:v:31:y:2021:i:1:p:61-76:id:1554
    DOI: 10.37190/ord210105
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/1554%20-%20published.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.37190/ord210105?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2019. "The Remarkable Incidence of Congestion in Production: A Review, Empirical Illustration, and Research Agenda," Data Envelopment Analysis Journal, now publishers, vol. 4(2), pages 109-147, December.
    2. Cooper, W. W. & Deng, Honghui & Huang, Zhimin M. & Li, Susan X., 2002. "A one-model approach to congestion in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 36(4), pages 231-238, December.
    3. Wei, Quanling & Yan, Hong, 2004. "Congestion and returns to scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 153(3), pages 641-660, March.
    4. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saber Saati & Maryam Shadab, 2023. "Exploring congestion in intermediate products by DEA: an application on Iranian cement supply chain," Operational Research, Springer, vol. 23(4), pages 1-32, December.
    2. Maryam Shadab & Saber Saati & Reza Farzipoor Saen & Mohammad Khoveyni & Amin Mostafaee, 2021. "Detecting congestion in DEA by solving one model," Operations Research and Decisions, Wroclaw University of Science Technology, Faculty of Management, vol. 31, pages 77-96.
    3. Maryam Shadab & Saber Saati & Reza Farzipoor Saen & Mohammad Khoveyni & Amin Mostafaee, 2021. "Detecting congestion in DEA by solving one model," Operations Research and Decisions, Wroclaw University of Science Technology, Faculty of Management, vol. 31(1), pages 77-96.
    4. 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.
    5. 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.
    6. Fang, Zhong & Luo, Na & Xiao, Qiqi & Chiu, Yung-ho, 2025. "The performance and input congestion of 19 listed port companies in China," Transport Policy, Elsevier, vol. 164(C), pages 178-195.
    7. 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.
    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. 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. 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).
    11. 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.
    12. 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.
    13. Chen, Ci & Yan, Hong, 2011. "Network DEA model for supply chain performance evaluation," European Journal of Operational Research, Elsevier, vol. 213(1), pages 147-155, August.
    14. 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).
    15. 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.
    16. Sueyoshi, Toshiyuki & Shang, Jennifer & Chiang, Wen-Chyuan, 2009. "A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP," European Journal of Operational Research, Elsevier, vol. 199(1), pages 219-231, November.
    17. Pang, Qinghua & Qiu, Man & Zhang, Lina & Chiu, Yung-ho, 2023. "Congestion effects of energy and capital in China's carbon emission reduction: Evidence from provincial levels," Energy, Elsevier, vol. 274(C).
    18. Alfonso Mendoza-Velázquez & Francisco Benita, 2019. "Efficiency, Productivity, and Congestion Performance: Analysis of the Automotive Cluster in Mexico," Journal of Industry, Competition and Trade, Springer, vol. 19(4), pages 661-678, December.
    19. Antonio Affuso & Cataldo Ferrarese & Guido Nannariello, 2013. "Spending Review: un?analisi di efficienza delle Capitanerie di porto," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2013(3), pages 111-143.
    20. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wut:journl:v:31:y:2021:i:1:p:61-76:id:1554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.