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A DEA two-stage decision processes with shared resources

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  • Alireza Amirteimoori

Abstract

Data envelopment analysis (DEA) is an approach for measuring the relative efficiency of peer decision making units that have multiple inputs and outputs. In most practical applications of DEA presented in the literature, the presented models assume that outputs are produced perfectly (see Charnes et al. Eur J Oper Res 2:429–444, 1978 ). However, in many real situations, some outputs are imperfect and they need to be repaired. This paper develops a DEA approach for measuring the efficiency of decision processes which can be divided into two interdependent stages, arranged in series. The novelty of the proposed approach is the existence of perfect and imperfect outputs in a two-stage decision process. This application of two-stage process involves shared resources and the paper gives a best split of these shared resources between two stages. The case of Iranian car representatives is presented. Copyright Springer-Verlag 2013

Suggested Citation

  • Alireza Amirteimoori, 2013. "A DEA two-stage decision processes with shared resources," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(1), pages 141-151, January.
    • Handle: RePEc:spr:cejnor:v:21:y:2013:i:1:p:141-151
    DOI: 10.1007/s10100-011-0218-3
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    References listed on IDEAS

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    1. 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.
    2. Alireza Amirteimoori & Sohrab Kordrostami, 2010. "Multi-period efficiency analysis in data envelopment analysis," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 2(1), pages 113-128.
    3. 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.
    4. Lawrence M. Seiford & Joe Zhu, 1999. "Profitability and Marketability of the Top 55 U.S. Commercial Banks," Management Science, INFORMS, vol. 45(9), pages 1270-1288, September.
    5. Kao, Chiang, 2009. "Efficiency measurement for parallel production systems," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1107-1112, August.
    6. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, January.
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    Cited by:

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    2. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    3. Adel Hatami-Marbini & Saber Saati & Seyed Mojtaba Sajadi, 2018. "Efficiency analysis in two-stage structures using fuzzy data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 909-932, December.
    4. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2020. "Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"," Technovation, Elsevier, vol. 94.
    5. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2018. "Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain," Technovation, Elsevier, vol. 74, pages 42-53.
    6. Jing Fu, 2018. "Two-stage data envelopment analysis with undesirable intermediate measures: an application to air quality improvement in China," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 861-885, December.
    7. Yang, Min & Zhang, Maolin & Yin, Pengzhen & Liang, Liang, 2023. "Performance evaluation of scientific research system in Chinese universities: A view of goal congruence," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    8. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    9. Wu, Jie & Zhu, Qingyuan & Ji, Xiang & Chu, Junfei & Liang, Liang, 2016. "Two-stage network processes with shared resources and resources recovered from undesirable outputs," European Journal of Operational Research, Elsevier, vol. 251(1), pages 182-197.
    10. Mohammad Nemati & Reza Kazemi Matin & Mehdi Toloo, 2020. "A two-stage DEA model with partial impacts between inputs and outputs: application in refinery industries," Annals of Operations Research, Springer, vol. 295(1), pages 285-312, December.
    11. Junfei Chu & Jie Wu & Qingyuan Zhu & Qingxian An & Beibei Xiong, 2019. "Analysis of China’s Regional Eco-efficiency: A DEA Two-stage Network Approach with Equitable Efficiency Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1263-1285, December.
    12. Zhao, Linlin & Zha, Yong & Zhuang, Yuliang & Liang, Liang, 2019. "Data envelopment analysis for sustainability evaluation in China: Tackling the economic, environmental, and social dimensions," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1083-1095.

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