Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning

Jaike van Twiller; Djordje Grbic; Rune Møller Jensen

doi:10.1007/978-3-031-43612-3_6

Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning

Machine Learning

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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.

Originalsprog	Engelsk
Titel	ICCL 2023: Computational Logistics
Redaktører	J.R. Daduna, G. Liedtke, X. Shi, S. Voß
Antal sider	17
Vol/bind	14239
Udgivelsessted	Berlin
Forlag	Springer
Publikationsdato	7 sep. 2023
Sider	105-121
Artikelnummer	6
Kapitel	Maritime Shipping
ISBN (Trykt)	978-3-031-43611-6
ISBN (Elektronisk)	978-3-031-43612-3
DOI	https://doi.org/10.1007/978-3-031-43612-3_6
Status	Udgivet - 7 sep. 2023
Begivenhed	International Conference on Computational Logistics - Berlin, Tyskland Varighed: 6 sep. 2023 → 8 sep. 2023 Konferencens nummer: 14 https://www.iccl2023.uni-hamburg.de/en/program.html

Konference

Konference	International Conference on Computational Logistics
Nummer	14
Land/Område	Tyskland
By	Berlin
Periode	06/09/2023 → 08/09/2023
Internetadresse	https://www.iccl2023.uni-hamburg.de/en/program.html

Navn	Lecture Notes in Computer Science
Vol/bind	14239
ISSN	0302-9743

Emneord

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

Adgang til dokumentet

10.1007/978-3-031-43612-3_6

978-3-031-43612-3_6Forlagets udgivne version, 1,52 MB

Citationsformater

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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",

note = "International Conference on Computational Logistics, ICCL ; Conference date: 06-09-2023 Through 08-09-2023",

url = "https://www.iccl2023.uni-hamburg.de/en/program.html",

}

van Twiller, J , Grbic, D & Jensen, RM 2023, Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning. i JR Daduna, G Liedtke, X Shi & S Voß (red), ICCL 2023: Computational Logistics. bind 14239, 6, Springer, Berlin, Lecture Notes in Computer Science, bind 14239, s. 105-121, International Conference on Computational Logistics, Berlin, Berlin, Tyskland, 06/09/2023. https://doi.org/10.1007/978-3-031-43612-3_6

Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning. / van Twiller, Jaike ; Grbic, Djordje ; Jensen, Rune Møller.
ICCL 2023: Computational Logistics. red. / J.R. Daduna; G. Liedtke; X. Shi; S. Voß. Bind 14239 Berlin: Springer, 2023. s. 105-121 6 (Lecture Notes in Computer Science, Bind 14239).

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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N2 - 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.

AB - 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.

KW - Maritime logistics

KW - Liner shipping

KW - Stowage planning

KW - Deep reinforcement learning

KW - Markov decision processes

KW - Maritime logistics

KW - Liner shipping

KW - Stowage planning

KW - Deep reinforcement learning

KW - Markov decision processes

U2 - 10.1007/978-3-031-43612-3_6

DO - 10.1007/978-3-031-43612-3_6

M3 - Article in proceedings

SN - 978-3-031-43611-6

VL - 14239

T3 - Lecture Notes in Computer Science

SP - 105

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BT - ICCL 2023: Computational Logistics

A2 - Daduna, J.R.

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ER -

Towards a Deep Reinforcement Learning Model of Master Bay Stowage Planning

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