IDEAS home Printed from https://ideas.repec.org/r/eee/ejores/v153y2004i3p641-660.html
   My bibliography  Save this item

Congestion and returns to scale in data envelopment analysis

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


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. Noor Ramli & Susila Munisamy & Behrouz Arabi, 2013. "Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector," Annals of Operations Research, Springer, vol. 211(1), pages 381-398, December.
  3. 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.
  4. Gong, Shixin & Shao, Cheng & Zhu, Li, 2017. "Energy efficiency evaluation in ethylene production process with respect to operation classification," Energy, Elsevier, vol. 118(C), pages 1370-1379.
  5. 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).
  6. 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.
  7. 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.
  8. Jing Huang & Dongqian Xue, 2019. "Study on Temporal and Spatial Variation Characteristics and Influencing Factors of Land Use Efficiency in Xi’an, China," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
  9. 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).
  10. Chen, Zhenling & Li, Jinkai & Zhao, Weigang & Yuan, Xiao-Chen & Yang, Guo-liang, 2019. "Undesirable and desirable energy congestion measurements for regional coal-fired power generation industry in China," Energy Policy, Elsevier, vol. 125(C), pages 122-134.
  11. 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.
  12. 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.
  13. František STŘELEČEK & Radek ZDENĚK & Jana LOSOSOVÁ, 2011. "Influence of production change on return to scale," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 57(4), pages 159-168.
  14. Ali Azadeh & Mansoureh Hasannia Kolaee & Vahid Salehi, 2016. "The impact of redundancy on resilience engineering in a petrochemical plant by data envelopment analysis," Journal of Risk and Reliability, , vol. 230(3), pages 285-296, June.
  15. 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.
  16. Nasierowski Wojciech & Arcelus Francisco J., 2012. "What is Innovativeness: Literature Review," Foundations of Management, Sciendo, vol. 4(1), pages 63-74, June.
  17. Antonio Peyrache & Angelo Zago, 2024. "The inefficiency of courts of justice: industry structure, capacity and misallocation," Journal of Productivity Analysis, Springer, vol. 62(2), pages 217-238, October.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. František Střeleček & Jana Lososová & Radek Zdeněk, 2010. "Size and structure of return to scale in revenue function and cost function [Velikost a struktura efektu z rozsahu ve výnosové a nákladové funkci]," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 58(6), pages 491-502.
  23. Junli Gao & Chaofeng Shao & Sihan Chen, 2022. "Evolution and Driving Factors of the Spatiotemporal Pattern of Tourism Efficiency at the Provincial Level in China Based on SBM–DEA Model," IJERPH, MDPI, vol. 19(16), pages 1-17, August.
  24. 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.
  25. Daohan Huang & Zihao Shen & Chengshuang Sun & Guijun Li, 2021. "Shifting from Production-Based to Consumption-Based Nexus Governance: Evidence from an Input–Output Analysis of the Local Water-Energy-Food Nexus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1673-1688, April.
  26. Peixin Duan, 2022. "How large of a grant size is appropriate? Evidence from the National Natural Science Foundation of China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-14, February.
  27. Wojciech Nasierowski, 2019. "Assessing Technical Efficiency Of Innovations In Canada: The Global Snapshot," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(03), pages 1-25, April.
  28. 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.
  29. 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).
  30. Qiang Hou & Meiou Wang & Xue Zhou, 2018. "Improved DEA Cross Efficiency Evaluation Method Based on Ideal and Anti-Ideal Points," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-9, April.
  31. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.