Model transformation framework for scheduling offshore logistics
Helena Sczcerbicka and
A chapter in Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability, 2020, pp 521-552 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management
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.
Keywords: Logistics; Industry 4.0; Supply Chain Management; Sustainability; City Logistics; Maritime Logistics; Data Science (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228961
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