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Dynamic DEA models with network structure: An application for Iranian airlines

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  • Omrani, Hashem
  • Soltanzadeh, Elham

Abstract

Efficiency estimation of interdependent divisions within a company or assessing the interrelated processes in a production system provides insights for improving the operational performance. Recent developments in network data envelopment analysis (NDEA) models enable decision making units (DMUs) to be informed of inefficient processes within the system. The NDEA model assesses the processes of the system in a specific moment and ignores the dynamic effects within the production processes. Thus, without considering the temporal dimension of production processes, biased efficiency measurement will be obtained that provides misleading information to DMUs. For evaluating the performance of a DMU with interrelated processes during specified multiple periods, this paper proposes a relational dynamic NDEA (DNDEA) model which measures the efficiencies of the system and its internal processes over the time, simultaneously. To illustrate the capability of the proposed model, this study for the first time measures the efficiency of eight Iranian airlines in several periods connected to each other by carry over flows. The actual data is gathered in three periods from 2010 to 2012 and the results are compared with the dynamic DEA and network DEA models in the same time span.

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  • Omrani, Hashem & Soltanzadeh, Elham, 2016. "Dynamic DEA models with network structure: An application for Iranian airlines," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 52-61.
    • Handle: RePEc:eee:jaitra:v:57:y:2016:i:c:p:52-61
    DOI: 10.1016/j.jairtraman.2016.07.014
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    Cited by:

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    18. Kottas, Angelos T. & Madas, Michael A., 2018. "Comparative efficiency analysis of major international airlines using Data Envelopment Analysis: Exploring effects of alliance membership and other operational efficiency determinants," Journal of Air Transport Management, Elsevier, vol. 70(C), pages 1-17.
    19. Shiping Mao & Marios Dominikos Kremantzis & Leonidas Sotirios Kyrgiakos & George Vlontzos, 2022. "R&D Performance Evaluation in the Chinese Food Manufacturing Industry Based on Dynamic DEA in the COVID-19 Era," Agriculture, MDPI, vol. 12(11), pages 1-19, November.
    20. Yu, Ming-Miin & Chen, Li-Hsueh & Chiang, Hui, 2017. "The effects of alliances and size on airlines’ dynamic operational performance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 197-214.
    21. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    22. Omrani, Hashem & Valipour, Mahsa & Emrouznejad, Ali, 2021. "A novel best worst method robust data envelopment analysis: Incorporating decision makers’ preferences in an uncertain environment," Operations Research Perspectives, Elsevier, vol. 8(C).
    23. Liu, Zhao & Zhang, Huan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2020. "How does industrial policy affect the eco-efficiency of industrial sector? Evidence from China," Applied Energy, Elsevier, vol. 272(C).
    24. Wu, Tai-Hsi & Huang, Shi-Wei & Lin, Mei-Chen & Wang, Hsin-Hua, 2023. "Energy security performance evaluation revisited: From the perspective of the energy supply chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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