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A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions

Author

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  • Andreas C. Georgiou

    (Quantitative Methods and Decision Analysis Lab, Department of Business Administration, University of Macedonia, GR-54636 Thessaloniki, Greece)

  • Konstantinos Kaparis

    (Quantitative Methods and Decision Analysis Lab, Department of Business Administration, University of Macedonia, GR-54636 Thessaloniki, Greece)

  • Eleni-Maria Vretta

    (Quantitative Methods and Decision Analysis Lab, Department of Business Administration, University of Macedonia, GR-54636 Thessaloniki, Greece)

  • Kyriakos Bitsis

    (Quantitative Methods and Decision Analysis Lab, Department of Business Administration, University of Macedonia, GR-54636 Thessaloniki, Greece)

  • George Paltayian

    (Quantitative Methods and Decision Analysis Lab, Department of Business Administration, University of Macedonia, GR-54636 Thessaloniki, Greece)

Abstract

The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector.

Suggested Citation

  • Andreas C. Georgiou & Konstantinos Kaparis & Eleni-Maria Vretta & Kyriakos Bitsis & George Paltayian, 2024. "A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions," Mathematics, MDPI, vol. 12(4), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:529-:d:1335815
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    References listed on IDEAS

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    1. 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.
    2. Afsharian, Mohsen & Ahn, Heinz & Thanassoulis, Emmanuel, 2017. "A DEA-based incentives system for centrally managed multi-unit organisations," European Journal of Operational Research, Elsevier, vol. 259(2), pages 587-598.
    3. Fang, Lei, 2013. "A generalized DEA model for centralized resource allocation," European Journal of Operational Research, Elsevier, vol. 228(2), pages 405-412.
    4. Beasley, J. E., 2003. "Allocating fixed costs and resources via data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 198-216, May.
    5. Portela, Maria Conceicao A. Silva & Thanassoulis, Emmanuel, 2007. "Comparative efficiency analysis of Portuguese bank branches," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1275-1288, March.
    6. Chen, Yao & Du, Juan & David Sherman, H. & Zhu, Joe, 2010. "DEA model with shared resources and efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(1), pages 339-349, November.
    7. Zha, Yong & Liang, Liang, 2010. "Two-stage cooperation model with input freely distributed among the stages," European Journal of Operational Research, Elsevier, vol. 205(2), pages 332-338, September.
    8. Chiang Kao, 2014. "Efficiency Decomposition in Network Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 55-77, Springer.
    9. Yu, Ming-Miin & Chern, Ching-Chin & Hsiao, Bo, 2013. "Human resource rightsizing using centralized data envelopment analysis: Evidence from Taiwan's Airports," Omega, Elsevier, vol. 41(1), pages 119-130.
    10. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    11. Athanassopoulos, Antreas D., 1995. "Goal programming & data envelopment analysis (GoDEA) for target-based multi-level planning: Allocating central grants to the Greek local authorities," European Journal of Operational Research, Elsevier, vol. 87(3), pages 535-550, December.
    12. Fukuyama, Hirofumi & Matousek, Roman, 2011. "Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 75-91, February.
    13. Sebastián Lozano & Gabriel Villa, 2004. "Centralized Resource Allocation Using Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 22(1), pages 143-161, July.
    14. Afsharian, Mohsen & Ahn, Heinz & Thanassoulis, Emmanuel, 2019. "A frontier-based system of incentives for units in organisations with varying degrees of decentralisation," European Journal of Operational Research, Elsevier, vol. 275(1), pages 224-237.
    15. Qiu, Qingan & Cui, Lirong & Gao, Hongda & Yi, He, 2018. "Optimal allocation of units in sequential probability series systems," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 351-363.
    16. 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.
    17. Wang, Ke & Huang, Wei & Wu, Jie & Liu, Ying-Nan, 2014. "Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA," Omega, Elsevier, vol. 44(C), pages 5-20.
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