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Artificial intelligence and operations research in maritime logistics

Citation Link: https://doi.org/10.15480/882.3140
Publikationstyp
Conference Paper
Date Issued
2020-09-23
Sprache
English
Author(s)
Dornemann, Jorin  orcid-logo
Rückert, Nicolas 
Fischer, Kathrin  orcid-logo
Taraz, Anusch  
Herausgeber*innen
Jahn, Carlos  orcid-logo
Kersten, Wolfgang  orcid-logo
Ringle, Christian M.  orcid-logo
Institut
Mathematik E-10  
Quantitative Unternehmensforschung und Wirtschaftsinformatik W-4  
TORE-DOI
10.15480/882.3140
TORE-URI
http://hdl.handle.net/11420/8045
First published in
Proceedings of the Hamburg International Conference of Logistics (HICL)  
Number in series
30
Start Page
337
End Page
381
Citation
Hamburg International Conference of Logistics (HICL) 30: 337-381 (2020)
Contribution to Conference
Hamburg International Conference of Logistics (HICL) 2020  
Publisher Link
https://www.epubli.de/shop/buch/Data-Science-in-Maritime-and-City-Logistics-Wolfgang-Kersten-9783753123479/106048
Publisher
epubli
Purpose: The application of artificial intelligence (AI) has the potential to lead to huge progress in combination with Operations Research methods. In our study, we explore current approaches for the usage of AI methods in solving optimization prob-lems. The aim is to give an overview of recent advances and to investigate how they are adapted to maritime logistics. Methodology: A structured literature review is conducted and presented. The iden-tified papers and contributions are categorized and classified, and the content and results of some especially relevant contributions are summarized. Moreover, an eval-uation, identifying existing research gaps and giving an outlook on future research directions, is given. Findings: Besides an inflationary use of AI keywords in the area of optimization, there has been growing interest in using machine learning to automatically learn heuristics for optimization problems. Our research shows that those approaches mostly have not yet been adapted to maritime logistics problems. The gaps identi-fied provide the basis for developing learning models for maritime logistics in future research. Originality: Using methods of machine learning in the area of operations research is a promising and active research field with a wide range of applications. To review these recent advances from a maritime logistics' point of view is a novel approach which could lead to advantages in developing solutions for large-scale optimization problems in maritime logistics in future research and practice.
Subjects
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
DDC Class
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by-sa/4.0/
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Dornemann et al. (2020) - Artificial Intelligence and Operations Research in Maritime Logistics.pdf

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