Abstract
In order to allow for truly holistic considerations, as intended in the Digital Factory concept, energy-related factors need to be considered. This has not been widely implemented for discrete event simulation (ie, material flow simulation) in industrial companies, yet, even though it may foster the energy efficiency within production sites considerably. A primary reason for the lack of acceptance is that previously discussed approaches did not meet the users’ requirements. This paper discusses how a suitable extension (eniBRIC), which renders energy-related considerations possible within material flow simulation, can be developed paying heed to both user requirements and the state of the art. A special focus is set on its implementation in Siemens Tecnomatix Plant Simulation. The workflow for the integration of the extension into existing and new simulation models is outlined. Opportunities for its utilisation in specific application examples, as well as the associated extra time and effort are discussed.
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Acknowledgements
Note—This paper is a revised and expanded version of a paper entitled Erweiterte Integration energetischer Betrachtungen in der Materialflusssimulation presented at 15th ASIM Dedicated Conference Simulation in Production and Logistics, Paderborn, Germany, 9–11 October 2013.
The presented work summarises outcomes of the research projects InnoCaT® and eniPROD®. The pre-competetive joint research project ‘Innovation Alliance Green Carbody Technologies’ (InnoCaT®) is funded by the ‘Bundesministerium für Bildung und Forschung (BMBF)’ (funding mark 02PO2700 ff) and supervised by ‘Projektträger Karlsruhe (PTKA)’. The authors are responsible for the content of the publication. The Cluster of Excellence ‘Energy-Efficient Product and Process Innovation in Production Engineering’ (eniPROD®) is funded by the European Union (European Regional Development Fund) and the Free State of Saxony.
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Stoldt, J., Schlegel, A. & Putz, M. Enhanced integration of energy-related considerations in discrete event simulation for manufacturing applications. J Simulation 10, 113–122 (2016). https://doi.org/10.1057/jos.2015.24
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DOI: https://doi.org/10.1057/jos.2015.24