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Best output prediction in OECD railways using DEA in conjunction with machine learning algorithms

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  • Süleyman Çakır

    (Recep Tayyip Erdoğan University)

Abstract

Efficiency measurement plays an increasingly important role in the regulation and management of railway organizations. Despite its proven usefulness in efficiency measurement, data envelopment analysis (DEA) lacks predictive capability. In order to benefit from their learning and mapping capabilities, machine learning (ML) algorithms have been used as a complementary method to DEA, recently. However, the majority of the existing ML-DEA studies focused on efficiency estimation while disregarding the prediction of DEA projected inputs/outputs toward better performance. This study proposes a novel framework using the adaptive neuro-fuzzy inference system (ANFIS) and the support vector machines (SVM) models in conjunction with the context-dependent DEA model to predict efficiency scores and the best input/output levels for 37 railway companies of OECD countries. Despite drawing on a small sample size, the proposed DEA-ANFIS and DEA-SVM models successfully predicted the efficiency scores and the best output levels of the organizations via approximating the efficient frontiers.

Suggested Citation

  • Süleyman Çakır, 2024. "Best output prediction in OECD railways using DEA in conjunction with machine learning algorithms," Annals of Operations Research, Springer, vol. 335(1), pages 59-77, April.
    • Handle: RePEc:spr:annopr:v:335:y:2024:i:1:d:10.1007_s10479-023-05668-w
    DOI: 10.1007/s10479-023-05668-w
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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. Christian Growitsch & Heike Wetzel, 2009. "Testing for Economies of Scope in European Railways: An Efficiency Analysis," Journal of Transport Economics and Policy, University of Bath, vol. 43(1), pages 1-24, January.
    3. Olli‐Pekka Hilmola, 2007. "European railway freight transportation and adaptation to demand decline," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 56(3), pages 205-225, March.
    4. Ye Tian & Jiangfeng Li, 2023. "Improvement Pathways for Urban Land Use Efficiency in the Beijing-Tianjin-Hebei Urban Agglomeration at the County Level: A Context-Dependent DEA Based on the Closest Target," IJERPH, MDPI, vol. 20(5), pages 1-22, March.
    5. 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.
    6. Congliang Wu & Ning Zhang & Liwen Xu, 2021. "Travelers on the Railway: An Economic Growth Model of the Effects of Railway Transportation Infrastructure on Consumption and Sustainable Economic Growth," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    7. Chen, Zhongfei & Matousek, Roman & Wanke, Peter, 2018. "Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 71-86.
    8. Lerida-Navarro, Carlos & Nombela, Gustavo & Tranchez-Martin, Jose M., 2019. "European railways: Liberalization and productive efficiency," Transport Policy, Elsevier, vol. 83(C), pages 57-67.
    9. Mohammad Izadikhah & Reza Farzipoor Saen, 2020. "Ranking sustainable suppliers by context-dependent data envelopment analysis," Annals of Operations Research, Springer, vol. 293(2), pages 607-637, October.
    10. Seiford, Lawrence M. & Zhu, Joe, 2003. "Context-dependent data envelopment analysis--Measuring attractiveness and progress," Omega, Elsevier, vol. 31(5), pages 397-408, October.
    11. Barros, Carlos Pestana & Wanke, Peter, 2015. "An analysis of African airlines efficiency with two-stage TOPSIS and neural networks," Journal of Air Transport Management, Elsevier, vol. 44, pages 90-102.
    12. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    13. Yao Chen & Hiroshi Morita & Joe Zhu, 2005. "Context-Dependent Dea With An Application To Tokyo Public Libraries," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 385-394.
    14. Peter Wanke & Md. Abul Kalam Azad, 2018. "Efficiency in Asian railways: a comparison between data envelopment analysis approaches," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(6), pages 573-599, August.
    15. Tomikawa, Tadaaki & Goto, Mika, 2022. "Efficiency assessment of Japanese National Railways before and after privatization and divestiture using data envelopment analysis," Transport Policy, Elsevier, vol. 118(C), pages 44-55.
    16. Mette Asmild & Torben Holvad & Jens Hougaard & Dorte Kronborg, 2009. "Railway reforms: do they influence operating efficiency?," Transportation, Springer, vol. 36(5), pages 617-638, September.
    17. Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
    18. Seda Busra Sarac & Kazim Baris Atici & Aydin Ulucan, 2022. "Elasticity measurement on multiple levels of DEA frontiers: an application to agriculture," Journal of Productivity Analysis, Springer, vol. 57(3), pages 313-324, June.
    19. Le, Yiping & Oka, Minami & Kato, Hironori, 2022. "Efficiencies of the urban railway lines incorporating financial performance and in-vehicle congestion in the Tokyo Metropolitan Area," Transport Policy, Elsevier, vol. 116(C), pages 343-354.
    20. Douglas W. Caves & Laurits R. Christensen & Joseph A. Swanson, 1981. "Economic Performance in Regulated and Unregulated Environments: A Comparison of U. S. and Canadian Railroads," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 96(4), pages 559-581.
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    More about this item

    Keywords

    Adaptive neuro-fuzzy inference system; Support vector machines; Data envelopment analysis; Railway companies;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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