@inproceedings{3f8da0cf0e964f0da4282e9d50067227,
title = "Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning",
abstract = "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.",
keywords = "Maritime logistics, Liner shipping, Stowage planning, Deep reinforcement learning, Markov decision processes, Maritime logistics, Liner shipping, Stowage planning, Deep reinforcement learning, Markov decision processes",
author = "{van Twiller}, Jaike and Djordje Grbic and Jensen, {Rune M{\o}ller}",
year = "2023",
month = sep,
day = "7",
doi = "10.1007/978-3-031-43612-3_6",
language = "English",
isbn = "978-3-031-43611-6",
volume = "14239",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "105--121",
editor = "J.R. Daduna and G. Liedtke and X. Shi and S. Vo{\ss}",
booktitle = "ICCL 2023: Computational Logistics",
address = "Germany",
}