Optimizing inland container shipping through reinforcement learning

Vid Tomljenovic, Yasemin Merzifonluoglu () and Giacomo Spigler
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Vid Tomljenovic: Tilburg University
Yasemin Merzifonluoglu: Tilburg University
Giacomo Spigler: Tilburg University

Annals of Operations Research, 2024, vol. 339, issue 1, No 37, 1025-1050

Abstract: 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.

Keywords: Reinforcement learning; Dynamic fleet assignment; Inland logistics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-024-05927-4

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