Spatial arrangement using deep reinforcement learning to minimise rearrangement in ship block stockyards-Reference-Cited by-同舟云学术

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Spatial arrangement using deep reinforcement learning to minimise rearrangement in ship block stockyards

Published:2020-04-20 Issue:16 Volume:58 Page:5062-5076
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Kim Byeongseop¹,Jeong Yongkuk²^ORCID,Shin Jong Gye¹³

Affiliation:

1. Department of Naval Architecture and Ocean Engineering, Seoul National University;

2. Department of Sustainable Production Development, KTH Royal Institute of Technology;

3. Research Institute of Marine System Engineering, Seoul National University

Funder

Ministry of Trade, Industry and Energy

National IT Industry Promotion Agency

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2020.1748247

Reference23 articles.

1. A genetic algorithm to solve the storage space allocation problem in a container terminal

2. Q(λ) learning-based dynamic route guidance algorithm for overhead hoist transport systems in semiconductor fabs

3. An analysis of shipyard spatial arrangement planning problems and a spatial arrangement algorithm considering free space and unplaced block

4. Reinforcement Learning: A Survey

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