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Artificial intelligence and operations research in maritime logistics

In: Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30

Author

Listed:
  • Dornemann, Jorin
  • Rückert, Nicolas
  • Fischer, Kathrin
  • Taraz, Anusch

Abstract

Purpose: The application of artificial intelligence (AI) has the potential to lead to huge progress in combination with Operations Research methods. In our study, we explore current approaches for the usage of AI methods in solving optimization problems. The aim is to give an overview of recent advances and to investigate how they are adapted to maritime logistics. Methodology: A structured literature review is conducted and presented. The identified papers and contributions are categorized and classified, and the content and results of some especially relevant contributions are summarized. Moreover, an evaluation, identifying existing research gaps and giving an outlook on future research directions, is given. Findings: Besides an inflationary use of AI keywords in the area of optimization, there has been growing interest in using machine learning to automatically learn heuristics for optimization problems. Our research shows that those approaches mostly have not yet been adapted to maritime logistics problems. The gaps identified provide the basis for developing learning models for maritime logistics in future research. Originality: Using methods of machine learning in the area of operations research is a promising and active research field with a wide range of applications. To review these recent advances from a maritime logistics' point of view is a novel approach which could lead to advantages in developing solutions for large-scale optimization problems in maritime logistics in future research and practice.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:hiclch:228955
    DOI: 10.15480/882.3140
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    References listed on IDEAS

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    1. Ulf Speer & Kathrin Fischer, 2017. "Scheduling of Different Automated Yard Crane Systems at Container Terminals," Transportation Science, INFORMS, vol. 51(1), pages 305-324, February.
    2. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    3. Qiang Meng & Shuaian Wang & Henrik Andersson & Kristian Thun, 2014. "Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions," Transportation Science, INFORMS, vol. 48(2), pages 265-280, May.
    4. Levent Kandiller, 2007. "Principles of Mathematics in Operations Research," International Series in Operations Research and Management Science, Springer, number 978-0-387-37735-3, December.
    5. 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.
    6. Alessandro Hill & Jürgen W. Böse, 2017. "A decision support system for improved resource planning and truck routing at logistic nodes," Information Technology and Management, Springer, vol. 18(3), pages 241-251, September.
    7. Sebastian Zurheide & Kathrin Fischer, 2012. "A revenue management slot allocation model for liner shipping networks," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(3), pages 334-361, September.
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    Cited by:

    1. Lorenz Kolley & Nicolas Rückert & Marvin Kastner & Carlos Jahn & Kathrin Fischer, 2023. "Robust berth scheduling using machine learning for vessel arrival time prediction," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 29-69, March.

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