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Title: Artificial intelligence and operations research in maritime logistics
Language: English
Authors: Dornemann, Jorin  
Rückert, Nicolas  
Fischer, Kathrin 
Taraz, Anusch 
Editor: Jahn, Carlos  
Kersten, Wolfgang  
Ringle, Christian M.  
Keywords: Logistics;Industry 4.0;Supply Chain Management;Sustainability;City Logistics;Maritime Logistics;Data Science
Issue Date: 23-Sep-2020
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 30: 337-381 (2020)
Abstract (english): 
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.
Conference: Hamburg International Conference of Logistics (HICL) 2020 
DOI: 10.15480/882.3140
ISBN: 978-3-753123-47-9
ISSN: 2365-5070
Institute: Mathematik E-10 
Quantitative Unternehmensforschung und Wirtschaftsinformatik W-4 
Document Type: Chapter/Article (Proceedings)
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 30
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