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A two-stage DEA model with partial impacts between inputs and outputs: application in refinery industries

Author

Listed:
  • Mohammad Nemati

    (Islamic Azad University)

  • Reza Kazemi Matin

    (Islamic Azad University)

  • Mehdi Toloo

    (VŠB-Technical University of Ostrava)

Abstract

Conventional data envelopment analysis (DEA) methods are useful for estimating the performance measure of decision making units (DMUs) that each DMU uses multiple inputs to produce multiple outputs without considering any partial impacts between inputs and outputs. Nevertheless, there are some real-world situations where DMUs may possess several production lines with a two-stage network structure that each production line use inputs according to their needs. The current paper extends the recent work by Ma (Expert Syst Appl Int J 42:4339–4347, 2015) to consider partial impact between inputs and outputs for two-stage network production systems. Toward this end, we consider several input–output bundles in each stage for production lines. We formulate a couple of new mathematical programming models in the DEA framework with the aim of considering partial impact between inputs and outputs for calculating aggregate, overall, and subunit efficiencies along with resource usage by production lines for a two-stage production system Finally, an application in refinery industries is provided as an example to illustrate the potential application of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:295:y:2020:i:1:d:10.1007_s10479-020-03665-x
    DOI: 10.1007/s10479-020-03665-x
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    Cited by:

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    2. Christina Bampatsou & George Halkos & Olga-Helen Astara, 2022. "Composite indicators in evaluating tourism performance and seasonality," Operational Research, Springer, vol. 22(3), pages 2373-2396, July.
    3. Hamid Kiaei & Reza Farzipoor Saen & Reza Kazemi Matin, 2023. "Cross-efficiency evaluation and improvement in two-stage network data envelopment analysis," Annals of Operations Research, Springer, vol. 321(1), pages 281-309, February.
    4. Sinem Buyuksaatci Kiris & Enes Eryarsoy & Selim Zaim & Dursun Delen, 2023. "An integrated approach for lean production using simulation and data envelopment analysis," Annals of Operations Research, Springer, vol. 320(2), pages 863-886, January.
    5. Hamid Kiaei & Reza Kazemi Matin, 2022. "New common set of weights method in black-box and two-stage data envelopment analysis," Annals of Operations Research, Springer, vol. 309(1), pages 143-162, February.
    6. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    7. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.

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