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Impact and relevance of transit disturbances on planning in intermodal container networks using disturbance cost analysis

Author

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  • Bart van Riessen

    (Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands. E-mails: vanriessen@ese.eur.nl; rdekker@ese.eur.nl
    Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands E-mails: r.r.negenborn@tudelft.nl; g.lodewijks@tudelft.nl)

  • Rudy R Negenborn

    (Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands E-mails: r.r.negenborn@tudelft.nl; g.lodewijks@tudelft.nl)

  • Gabriel Lodewijks

    (Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands E-mails: r.r.negenborn@tudelft.nl; g.lodewijks@tudelft.nl)

  • Rommert Dekker

    (Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands. E-mails: vanriessen@ese.eur.nl; rdekker@ese.eur.nl)

Abstract

In North-West Europe, the options for intermodal inland transportation of containers are increasing. Inland corridors become increasingly interconnected in hinterland networks. To minimise operating costs, new methods are required that allow integral network operations management. The network operations consist of allocating containers to available inland transportation services, that is, planning. For adequate planning it is important to adapt to occurring disturbances. In this article, a new mathematical model is proposed: the Linear Container Allocation model with Time-restrictions. This model is used for determining the influence of three main types of transit disturbances on network performance: early service departure, late service departure and cancellation of inland services. The influence of a disturbance is measured in two ways. The impact measures the additional cost incurred by an updated planning in case of a disturbance. The relevance measures the cost difference between a fully updated and a locally updated plan. With the results of the analysis, key service properties of disturbed services that result in a high impact or high relevance can be determined. Based on this, the network operator can select focus areas to prevent disturbances with high impact and to improve the planning updates in case of disturbances with high relevance. The proposed method is used in a case study to assess the impact and relevance of transit disturbances on inland services of the European Gateway Services network.

Suggested Citation

  • Bart van Riessen & Rudy R Negenborn & Gabriel Lodewijks & Rommert Dekker, 2015. "Impact and relevance of transit disturbances on planning in intermodal container networks using disturbance cost analysis," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 17(4), pages 440-463, December.
    • Handle: RePEc:pal:marecl:v:17:y:2015:i:4:p:440-463
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    Citations

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    Cited by:

    1. Bart Van Riessen & Judith Mulder & Rudy R. Negenborn & Rommert Dekker, 2021. "Revenue management with two fare classes in synchromodal container transportation," Flexible Services and Manufacturing Journal, Springer, vol. 33(3), pages 623-662, September.
    2. Qu, Wenhua & Rezaei, Jafar & Maknoon, Yousef & Tavasszy, Lóránt, 2019. "Hinterland freight transportation replanning model under the framework of synchromodality," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 308-328.
    3. Gumuskaya, Volkan & van Jaarsveld, Willem & Dijkman, Remco & Grefen, Paul & Veenstra, Albert, 2020. "Dynamic barge planning with stochastic container arrivals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    4. Beatriz Acero & Maria Jesus Saenz & Davide Luzzini, 2022. "Introducing synchromodality: One missing link between transportation and supply chain management," Journal of Supply Chain Management, Institute for Supply Management, vol. 58(1), pages 51-64, January.
    5. Sakti, Sekar & Zhang, Lele & Thompson, Russell G., 2023. "Synchronization in synchromodality," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    6. Steffen Jaap Bakker & E. Ruben van Beesten & Ingvild Synn{o}ve Brynildsen & Anette Sandvig & Marit Siqveland & Asgeir Tomasgard, 2023. "STraM: a framework for strategic national freight transport modeling," Papers 2304.14001, arXiv.org.
    7. Thibault Delbart & Yves Molenbruch & Kris Braekers & An Caris, 2021. "Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions," Sustainability, MDPI, vol. 13(7), pages 1-25, April.
    8. Li, Le & Negenborn, Rudy R. & De Schutter, Bart, 2017. "Distributed model predictive control for cooperative synchromodal freight transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 240-260.
    9. Behzad Behdani & Bart Wiegmans & Violeta Roso & Hercules Haralambides, 2020. "Port-hinterland transport and logistics: emerging trends and frontier research," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(1), pages 1-25, March.
    10. Akyüz, M. Hakan & Dekker, Rommert & Sharif Azadeh, Shadi, 2023. "Partial and complete replanning of an intermodal logistic system under disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).

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