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Issue title: Intelligent Systems, Applications and Environments for the Industry of the Future
Guest editors: Davy Preuveneers and Elisabeth Ilie-Zudor
Article type: Research Article
Authors: Bohács, Gábor; * | Rinkács, Angéla
Affiliations: Department of Material Handling and Logistic System, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economies, H-1111 Budapest, Műegyetem rakpart 3, Hungary
Correspondence: [*] Corresponding author. E-mail: [email protected].
Abstract: Simulation modelling has an ever increasing importance for complex systems. Manufacturing and related material flow or logistic systems are typical fields of application. Latest trends such as Cyber-physical systems and Industry 4.0 give a significant boost to simulation modelling as these require a digital model of the system. Complex manufacturing and related material flow systems are subject to frequent changes and pose a Big Data problem, which raises stronger requirements regarding self-adaptiveness. Conventional simulation models are to be adapted only via user interaction. Previous research steps have concentrated on the establishment of a novel simulation model structure, the so called “Jellyfish” model which unifies layout and process-type simulation models. Visualization of both aspects simultaneously enables interacting users to better understand the systems’ operation compared to the conventional models. The current paper focuses on the adaptive capability of the new model. We have concentrated on the hardest type of adaptation, the structural adaptation. In this paper, an ontology-driven component based approach is presented and explained further through an example. Application of automated ontology-matching in simulation environment is a novel approach enabling the simulation model to adapt its structure without the necessity of manual interaction.
Keywords: Simulation, logistics, ontologies, adaptive systems
DOI: 10.3233/AIS-170437
Journal: Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 3, pp. 361-374, 2017
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