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Optimizing inland container shipping through reinforcement learning

Author

Listed:
  • Vid Tomljenovic

    (Tilburg University)

  • Yasemin Merzifonluoglu

    (Tilburg University)

  • Giacomo Spigler

    (Tilburg University)

Abstract

In this study, we investigate the container delivery problem and explore ways to optimize the complex and nuanced system of inland container shipping. Our aim is to fulfill customer demand while maximizing customer service and minimizing logistics costs. To address the challenges posed by an unpredictable and rapidly-evolving environment, we examine the potential of leveraging reinforcement learning (RL) to automate the decision-making process and craft agile, efficient delivery schedules. Through a rigorous and comprehensive numerical study, we evaluate the efficacy of this approach by comparing the performance of several high-performance heuristic policies with that of agents trained using reinforcement learning, under various problem settings. Our results demonstrate that a reinforcement learning approach is robust and particularly useful for decision makers who must match logistics demand with capacity dynamically and have multiple objectives.

Suggested Citation

  • Vid Tomljenovic & Yasemin Merzifonluoglu & Giacomo Spigler, 2024. "Optimizing inland container shipping through reinforcement learning," Annals of Operations Research, Springer, vol. 339(1), pages 1025-1050, August.
    • Handle: RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-024-05927-4
    DOI: 10.1007/s10479-024-05927-4
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Azalia Mirhoseini & Anna Goldie & Mustafa Yazgan & Joe Wenjie Jiang & Ebrahim Songhori & Shen Wang & Young-Joon Lee & Eric Johnson & Omkar Pathak & Azade Nova & Jiwoo Pak & Andy Tong & Kavya Srinivasa, 2021. "A graph placement methodology for fast chip design," Nature, Nature, vol. 594(7862), pages 207-212, June.
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