Grosch, Benedikt ; Weitzel, Timm ; Panten, Niklas ; Abele, Eberhard (2019)
A Metaheuristic for Energy Adaptive Production Scheduling with Multiple Energy Carriers and its Implementation in a Real Production System 2.
In: Procedia CIRP, 26th CIRP Life Cycle Engineering Conference, West Lafayette, IN (USA), 80
doi: 10.1016/j.procir.2019.01.043
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Due to climate change and the resulting introduction of sustainability goals by the UN and federal governments, there is growing pressure on manufacturers to increase the sustainability of production systems. In this paper a new, sustainable production scheduling model for job-shop scheduling is developed. The model is optimized using an adjusted genetic algorithm (GA) to minimize energy-related cost (ERC). The proposed model includes multiple energy sources and incorporates a time-of-use (TOU) demand response (DR) scheme for all energy sources. Furthermore, it considers five machine operating modes to reflect different energy states of machines. This means that underutilized machines can be powered down to use less energy, thus reducing ERC. The model and algorithm are evaluated within the Energy-Technology and Application (ETA) research factory environment using a Python application that interfaces with other components to get information about the production system.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Grosch, Benedikt ; Weitzel, Timm ; Panten, Niklas ; Abele, Eberhard |
| Art des Eintrags: | Bibliographie |
| Titel: | A Metaheuristic for Energy Adaptive Production Scheduling with Multiple Energy Carriers and its Implementation in a Real Production System 2 |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP, 26th CIRP Life Cycle Engineering Conference, West Lafayette, IN (USA) |
| Jahrgang/Volume einer Zeitschrift: | 80 |
| DOI: | 10.1016/j.procir.2019.01.043 |
| Kurzbeschreibung (Abstract): | Due to climate change and the resulting introduction of sustainability goals by the UN and federal governments, there is growing pressure on manufacturers to increase the sustainability of production systems. In this paper a new, sustainable production scheduling model for job-shop scheduling is developed. The model is optimized using an adjusted genetic algorithm (GA) to minimize energy-related cost (ERC). The proposed model includes multiple energy sources and incorporates a time-of-use (TOU) demand response (DR) scheme for all energy sources. Furthermore, it considers five machine operating modes to reflect different energy states of machines. This means that underutilized machines can be powered down to use less energy, thus reducing ERC. The model and algorithm are evaluated within the Energy-Technology and Application (ETA) research factory environment using a Python application that interfaces with other components to get information about the production system. |
| Freie Schlagworte: | Energy Efficiency;Energy Flexibility;Genetic Algorithm;Metaheuristic;Optimization;Production Scheduling |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > ETA Energietechnologien und Anwendungen in der Produktion |
| Hinterlegungsdatum: | 25 Jun 2019 05:33 |
| Letzte Änderung: | 25 Jun 2019 05:33 |
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