Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Major liner shipping companies aim to solve the stowage planning problem by optimally allocating containers to vessel locations during a multi-port voyage. Due to a large variety of combinatorial aspects, a scalable algorithm to solve a representative problem is yet to be found. This paper will show that deep reinforcement learning can optimize a non-trivial master bay planning problem. Our experiments show that proximal policy optimization efficiently finds reasonable solutions, serving as preliminary evidence of the potential value of deep reinforcement learning in stowage planning. In future work, we will extend our architecture to address a full-featured master bay planning problem.
Original languageEnglish
Title of host publicationICCL 2023: Computational Logistics
EditorsJ.R. Daduna, G. Liedtke, X. Shi, S. Voß
Number of pages17
Place of PublicationBerlin
Publication date7 Sept 2023
Article number6
ChapterMaritime Shipping
ISBN (Print)978-3-031-43611-6
ISBN (Electronic)978-3-031-43612-3
Publication statusPublished - 7 Sept 2023
SeriesLecture Notes in Computer Science


  • Maritime logistics
  • Liner shipping
  • Stowage planning
  • Deep reinforcement learning
  • Markov decision processes


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