Simulative Analyse für die Rekonfiguration von Plug & Produce-Produktionssystemen unter Anwendung des NSGA2-Algorithmus
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Simulation in Produktion und Logistik 2025: Tagungsband 21. ASIM-Fachtagung Simulation in Produktion und Logistik, Dresden, 24. bis 26. September 2025. ed. / Sebastian Rank; Matthias Kühn; Torsten Schmidt. Dresden: Dresden University of Technology, 2025. p. 473-482 (ASIM-Mitteilung; Vol. 194).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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RIS
TY - CHAP
T1 - Simulative Analyse für die Rekonfiguration von Plug & Produce-Produktionssystemen unter Anwendung des NSGA2-Algorithmus
AU - Prüfer, Ole Christian
AU - Heger, Jens
PY - 2025/10/21
Y1 - 2025/10/21
N2 - Individualized products and short delivery times require production systems to be designed more reactively in order to meet these requirements. Plug & Produce (P&P) production systems are a concept that addresses these requirements. Short set-up times due to standardized interfaces make it possible to reconfigure production dynamically. This study simulates scalable P&P systems with different entities and analyzes their performance across multiple scenarios. Therefore, a discrete event simulation approach is applied to accurately model the dynamic behavior and interactions within the system. The focus is on reconfiguration planning using the Non-dominated Sorting Genetic Algorithm 2 (NSGA2). NSGA2 identifies optimal trade-off solutions for multi-objective optimization problems with competing objectives. This work concentrates on a systematic evaluation of NSGA2 in terms of its applicability, parameter tuning, and influence on P&P production scenarios.
AB - Individualized products and short delivery times require production systems to be designed more reactively in order to meet these requirements. Plug & Produce (P&P) production systems are a concept that addresses these requirements. Short set-up times due to standardized interfaces make it possible to reconfigure production dynamically. This study simulates scalable P&P systems with different entities and analyzes their performance across multiple scenarios. Therefore, a discrete event simulation approach is applied to accurately model the dynamic behavior and interactions within the system. The focus is on reconfiguration planning using the Non-dominated Sorting Genetic Algorithm 2 (NSGA2). NSGA2 identifies optimal trade-off solutions for multi-objective optimization problems with competing objectives. This work concentrates on a systematic evaluation of NSGA2 in terms of its applicability, parameter tuning, and influence on P&P production scenarios.
KW - Ingenieurwissenschaften
U2 - 10.25368/2025.279
DO - 10.25368/2025.279
M3 - Aufsätze in Konferenzbänden
SN - 978-3-86780-806-4
T3 - ASIM-Mitteilung
SP - 473
EP - 482
BT - Simulation in Produktion und Logistik 2025
A2 - Rank, Sebastian
A2 - Kühn, Matthias
A2 - Schmidt, Torsten
PB - Dresden University of Technology
CY - Dresden
ER -