Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228961
Authors: 
Rippel, Daniel
Peng, Shengrui
Lütjen, Michael
Sczcerbicka, Helena
Freitag, Michael
Year of Publication: 
2020
Citation: 
[Editor:] Jahn, Carlos [Editor:] Kersten, Wolfgang [Editor:] Ringle, Christian M. [Title:] Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30 [ISBN:] 978-3-7531-2347-9 [Year:] 2020 [Pages:] 521-552
Abstract: 
Purpose: Wind energy is a promising technology to produce sustainable energy. While higher wind speeds at sea result in higher energy production, they also impede the installation of wind farms. Several authors proposed optimization- or simulation-based scheduling models. This article provides a framework to instantiate different models and discusses their advantages and disadvantages using selected models from the literature. Methodology: Building upon previous research, which deducted a common meta-model by analyzing current literature, the framework realizes this model using the OMG's Essential Meta-Object Facility Standard. Moreover, the framework uses the OMG's Model To Text Transformation Language for transformations to different models found in the literature and from previous work, to evaluate their behavior given the same base-scenario. Findings: The results show that the proposed framework achieves an instantiation of different model types, i.e., a mathematical optimization, a multi-agent simulation, and a Petri-Nets-based simulation. The discussion highlights the advantages of these types regarding speed, optimality, and flexibility. As the primary advantage, this framework allows investigating the installation on varying levels, focusing on local resources, processes, or the global system. Originality: This research aims to operationalize a common meta-model and model transformations between different model formulations by applying well-established standards to realize a basis for using these models during the planning and schedul-ing of offshore activities. To the authors' best knowledge, no comparable work on the integration of different modeling techniques in the area of offshore logistics exists.
Subjects: 
Logistics
Industry 4.0
Supply Chain Management
Sustainability
City Logistics
Maritime Logistics
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

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