IDEAS home Printed from https://ideas.repec.org/h/zbw/hiclch/267188.html
   My bibliography  Save this book chapter

Workload forecasting of a logistic node using Bayesian neural networks

In: Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New Era. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 33

Author

Listed:
  • Nakilcioğlu, Emin
  • Rizvanolli, Anisa
  • Rendel, Olaf

Abstract

Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays a critical role in improving the work efficiency. This paper studies the relevant literature and designs a forecasting model addressing the aforementioned issues. Methodology: The paper develops a forecasting model to predict hourly work and traffic volume of container trucks in an empty container depot using a Bayesian Neural Network based model. Furthermore, the paper experiments with datasets with different characteristics to assess the model's forecasting range for various data sources. Findings: The real data of an empty container depot is utilized to develop a forecasting model and to later verify the capabilities of the model. The findings show the performance validity of the model and provide the groundwork to build an effective traffic and workload planning system for the empty container depot in question. Originality: This paper proposes a Bayesian deep learning-based forecasting model for traffic and workload of an empty container depot using real-world data. This designed and implemented forecasting model offers a solution with which every actor in the container truck transportation benefits from the optimized workload.

Suggested Citation

  • Nakilcioğlu, Emin & Rizvanolli, Anisa & Rendel, Olaf, 2022. "Workload forecasting of a logistic node using Bayesian neural networks," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 237-264, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:267188
    DOI: 10.15480/882.4694
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/267188/1/hicl-2021-33-237.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.15480/882.4694?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert Goldfarb & H. O. Stekler & Joel David, 2005. "Methodological issues in forecasting: Insights from the egregious business forecast errors of late 1930," Journal of Economic Methodology, Taylor & Francis Journals, vol. 12(4), pages 517-542.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    2. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    3. Stekler, Herman & Symington, Hilary, 2016. "Evaluating qualitative forecasts: The FOMC minutes, 2006–2010," International Journal of Forecasting, Elsevier, vol. 32(2), pages 559-570.
    4. Gabriel Mathy & Christian Roatta, 2018. "Forecasting the 1937-1938 Recession: Quantifying Contemporary Newspaper Forecasts," Working Papers 2018-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Sinclair Tara M, 2009. "Asymmetry in the Business Cycle: Friedman's Plucking Model with Correlated Innovations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(1), pages 1-31, December.
    6. Gabriel Mathy & Herman Stekler, 2018. "Was the deflation of the depression anticipated? An inference using real-time data," Journal of Economic Methodology, Taylor & Francis Journals, vol. 25(2), pages 117-125, April.
    7. Mathy, Gabriel & Stekler, Herman, 2017. "Expectations and forecasting during the Great Depression: Real-time evidence from the business press," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 1-15.
    8. Emma Catalfamo, 2018. "French Nowcasts of the US Economy during the Great Recession: A Textual Analysis," Working Papers 2018-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Green, Kesten C. & Armstrong, J. Scott, 2007. "Structured analogies for forecasting," International Journal of Forecasting, Elsevier, vol. 23(3), pages 365-376.
    10. Kathryn Lundquist & H.O. Stekler, 2011. "The Forecasting Performance of Business Economists During the Great Recession," Working Papers 2011-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    11. Herman O. Stekler & Raj M. Talwar, 2011. "Economic Forecasting in the Great Recession," Working Papers 2011-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    12. Jones, Jacob T. & Sinclair, Tara M. & Stekler, Herman O., 2020. "A textual analysis of Bank of England growth forecasts," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1478-1487.
    13. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    14. Bespalova, Olga, 2020. "GDP forecasts: Informational asymmetry of the SPF and FOMC minutes," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1531-1540.
    15. Kovacs Kevin & Boulier Bryan & Stekler Herman, 2017. "Nowcasting: Identifying German Cyclical Turning Points," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(4), pages 329-341, August.
    16. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    17. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    18. Tara M. Sinclair, 2019. "Continuities and Discontinuities in Economic Forecasting," Working Papers 2019-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    19. Herman O. Stekler & Hilary Symington, 2014. "How Did The Fomc View The Great Recession As It Was Happening?: Evaluating The Minutes From Fomc Meetings, 2006-2010," Working Papers 2014-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    More about this item

    Keywords

    Artificial Intelligence; Blockchain;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:hiclch:267188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://hicl.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.