Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning

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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.
OriginalsprogEngelsk
TitelICCL 2023: Computational Logistics
RedaktørerJ.R. Daduna, G. Liedtke, X. Shi, S. Voß
Antal sider17
Vol/bind14239
UdgivelsesstedBerlin
ForlagSpringer
Publikationsdato7 sep. 2023
Sider105-121
Artikelnummer6
KapitelMaritime Shipping
ISBN (Trykt)978-3-031-43611-6
ISBN (Elektronisk)978-3-031-43612-3
DOI
StatusUdgivet - 7 sep. 2023
NavnLecture Notes in Computer Science
Vol/bind14239
ISSN0302-9743

Emneord

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

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