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DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection

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  • Jie Wu
  • Junfei Chu
  • Qingyuan Zhu
  • Pengzhen Yin
  • Liang Liang

Abstract

Data envelopment analysis (DEA) has been extended to cross-efficiency evaluation to provide better discrimination and ranking of decision-making units (DMUs). However, the non-uniqueness of optimal weights in the traditional DEA models (CCR and BCC models) has reduced the usefulness of the DEA cross-efficiency evaluation method. To solve this problem, we introduce the concept of the satisfaction degree of a DMU towards a set of optimal weights for another DMU. Then, a new DEA cross-efficiency evaluation approach, which contains a maxmin model and two algorithms, is proposed based on the satisfaction degrees of the DMUs. Our maxmin model and algorithm 1 can obtain for each DMU an optimal set of weights that maximises the least satisfaction degrees among all the other DMUs. Further, our algorithm 2 can then be used to guarantee the uniqueness of the optimal weights for each DMU. Finally, our approach is applied to a real-world case study of technology selection.

Suggested Citation

  • Jie Wu & Junfei Chu & Qingyuan Zhu & Pengzhen Yin & Liang Liang, 2016. "DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 5990-6007, October.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:20:p:5990-6007
    DOI: 10.1080/00207543.2016.1148278
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    3. Xifan Chen & Qingyuan Zhu & Chengzhen Xu & Zhiyang Shen & Malin Song, 2024. "Energy and environmental efficiency of China's regional electric power industry by considering renewable energy constraints," Energy & Environment, , vol. 35(2), pages 927-949, March.
    4. Zhiying Zhang & Huchang Liao, 2024. "A stochastic cross-efficiency DEA approach based on the prospect theory and its application in winner determination in public procurement tenders," Annals of Operations Research, Springer, vol. 341(1), pages 509-537, October.
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    6. Karasakal, Esra & Aker, Pınar, 2017. "A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem," Omega, Elsevier, vol. 73(C), pages 79-92.
    7. Ali Homayoni & Reza Fallahnejad & Farhad Hosseinzadeh Lotfi, 2022. "Cross Malmquist Productivity Index in Data Envelopment Analysis," 4OR, Springer, vol. 20(4), pages 567-602, December.
    8. Alcaraz, Javier & Aparicio, Juan & Monge, Juan Fco & Ramón, Nuria, 2022. "Weight profiles in cross-efficiency evaluation based on hypervolume maximization," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    9. Majid Azadi & Mehdi Toloo & Fahimeh Ramezani & Reza Farzipoor Saen & Farookh Khadeer Hussain & Hajar Farnoudkia, 2024. "Evaluating efficiency of cloud service providers in era of digital technologies," Annals of Operations Research, Springer, vol. 342(2), pages 1049-1078, November.
    10. Wenli Liu & Ying-Ming Wang & Shulong Lv, 2017. "An aggressive game cross-efficiency evaluation in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 241-258, December.
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