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Real-time Container Transport Planning with Decision Trees based on Offline Obtained Optimal Solutions

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  • van Riessen, B.
  • Negenborn, R.R.
  • Dekker, R.

Abstract

Hinterland networks for container transportation require planning methods in order to increase efficiency and reliability of the inland road, rail and waterway connections. In this paper we aim to derive real-time decision rules for suitable allocations of containers to inland services by analysing the solution structure of a centralised optimisation method used offline on historic data. The decision tree can be used in a decision support system (DSS) for instantaneously allocating incoming orders to suitable services, without the need for continuous planning updates. Such a DSS is beneficial, as it is easy to implement in the current practice of container transportation. Earlier proposed centralised methods can find the optimal solution for the intermodal inland transportation problem in retrospect, but are not suitable when information becomes gradually available. The main contributions are threefold: firstly, a structured method for creating decision trees from optimal solutions is proposed. Secondly, an innovative method is used for obtaining multiple equivalent optimal solutions to prevent overfitting of the decision tree. And finally, a structured analysis of three error types is presented for assessing the quality of an obtained tree. A case study illustrates the method’s purpose by comparing the quality of the resulting plan with alternative methods.

Suggested Citation

  • van Riessen, B. & Negenborn, R.R. & Dekker, R., 2016. "Real-time Container Transport Planning with Decision Trees based on Offline Obtained Optimal Solutions," Econometric Institute Research Papers EI2016-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:79973
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    Cited by:

    1. Wenqing He & Zhihong Jin & Ying Huang & Shida Xu, 2021. "The Inland Container Transportation Problem with Separation Mode Considering Carbon Dioxide Emissions," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    2. Dornemann, Jorin & Rückert, Nicolas & Fischer, Kathrin & Taraz, Anusch, 2020. "Artificial intelligence and operations research in maritime logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 337-381, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. 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.
    4. Johannes Rentschler & Ralf Elbert & Felix Weber, 2022. "Promoting Sustainability through Synchromodal Transportation: A Systematic Literature Review and Future Fields of Research," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    5. 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).
    6. Guo, Wenjing & Atasoy, Bilge & van Blokland, Wouter Beelaerts & Negenborn, Rudy R., 2021. "Global synchromodal transport with dynamic and stochastic shipment matching," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    7. Sakti, Sekar & Zhang, Lele & Thompson, Russell G., 2023. "Synchronization in synchromodality," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    8. 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.
    9. Bernard G. Zweers & Sandjai Bhulai & Rob D. van der Mei, 2019. "Optimizing barge utilization in hinterland container transportation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(3), pages 253-271, April.
    10. Andreas Balster & Ole Hansen & Hanno Friedrich & André Ludwig, 2020. "An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(5), pages 403-416, October.
    11. Bouchery, Yann & Woxenius, Johan & Fransoo, Jan C., 2020. "Identifying the market areas of port-centric logistics and hinterland intermodal transportation," European Journal of Operational Research, Elsevier, vol. 285(2), pages 599-611.
    12. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    13. Hilde Heggen & Yves Molenbruch & An Caris & Kris Braekers, 2019. "Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions," Sustainability, MDPI, vol. 11(6), pages 1-36, March.
    14. Bernard G. Zweers & Sandjai Bhulai & Rob D. Mei, 2021. "Planning hinterland container transportation in congested deep-sea terminals," Flexible Services and Manufacturing Journal, Springer, vol. 33(3), pages 583-622, September.
    15. Bart Riessen & Rudy R. Negenborn & Rommert Dekker, 2017. "The Cargo Fare Class Mix problem for an intermodal corridor: revenue management in synchromodal container transportation," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 634-658, December.
    16. Giusti, Riccardo & Manerba, Daniele & Bruno, Giorgio & Tadei, Roberto, 2019. "Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 92-110.
    17. 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).
    18. Wenjing Guo & Bilge Atasoy & Wouter Beelaerts Blokland & Rudy R. Negenborn, 2022. "Anticipatory approach for dynamic and stochastic shipment matching in hinterland synchromodal transportation," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 483-517, June.
    19. Fan Bu & Heather Nachtmann, 2023. "Literature review and comparative analysis of inland waterways transport: “Container on Barge”," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 140-173, March.
    20. Mar-Ortiz, Julio & Castillo-García, Norberto & Gracia, María D., 2020. "A decision support system for a capacity management problem at a container terminal," International Journal of Production Economics, Elsevier, vol. 222(C).

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    Keywords

    Intermodal planning; synchromodal planning; container transportation; decision support; decision trees;
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