Zusammenfassung
An moderne Cyber-Physische Produktionssysteme werden hohe Anforderungen hinsichtlich Flexibilität und Effizienz gestellt. Flexibilität wird zum Teil bereits durch moderne Steuerungssoftware unterstützt, wobei die initialen Steuerungsparameter bisher typischerweise ad-hoc festgelegt werden. Dieser Beitrag stellt einen Ansatz zur Optimierung der initialen Steuerungsparameter vor, der mehrere Simulationsmodelle verschiedener Genauigkeit mittels ordinaler Transformation kombiniert. Die Anwendbarkeit des Ansatzes wird anhand eines Labordemonstrators für Produktionssysteme gezeigt.
Abstract
Modern cyber-physical production systems need to meet high demands regarding flexibility and efficiency. Even though flexibility is supported by modern control software, the initial control parameters are typically determined in an ad-hoc way. This paper presents an approach for optimizing the initial control parameters. This approach combines several simulation models of different fidelity, i. e. exactness, by means of ordinal transformation. The applicability of the approach is shown at hand of a demonstrator for production systems.
Funding source: Deutsche Forschungsgemeinschaft
Award Identifier / Grant number: VO 937/29-1
Award Identifier / Grant number: SPP 1593
Award Identifier / Grant number: SFB 768
Funding source: National Science Foundation
Award Identifier / Grant number: ECCS-1462409
Award Identifier / Grant number: CMMI-1462787
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 71720107003
Award Identifier / Grant number: 61603321
Funding statement: Diese Veröffentlichung wurde teilweise durch die Deutsche Forschungsgemeinschaft im Rahmen des Projekts „Domain-spanning Maintainability Estimation of Information and Manufacturing Automation Systems“ (DoMain, VO 937/29-1), Schwerpunktprogramm “Design for Future – Managed Software Evolution” (SPP 1593), sowie des Teilprojekts D1 des Sonderforschungsbereichs SFB 768 “Zyklenmanagement von Innovationsprozessen” gefördert. Zudem wurde diese Arbeit teilweise von der National Science Foundation im Rahmen der Auszeichnungen ECCS-1462409 und CMMI-1462787 und der National Natural Science Foundation of China im Rahmen von Grant 71720107003 und 61603321 finanziert.
About the authors
Minjie Zou, M.Sc., graduated in mechanical engineering from the Technical University of Munich (TUM) in 2016. She is a research assistant at the Institute of Automation and Information Systems at TUM and a member of the Collaborative Research Centre SFB 768. Her research interests include applying knowledge-based systems to verify and optimize automation engineering projects, model-based systems engineering, and inconsistency management.
Felix Ocker, M.Sc., is a graduate research assistant and Ph.D. student with the Institute of Automation and Information Systems at the Technical University of Munich. His research focuses on knowledge formalization and inconsistency management in interdisciplinary engineering.
Edward Huang received his B.S. degree in industrial engineering from National Tsinghua University, Hsinchu, Taiwan, and his Ph.D. degree in Industrial and Systems Engineering from Georgia Institute of Technology, Atlanta. He has been a senior systems engineer with Innovative Scheduling. Currently, he is an associate professor in the Department of Systems Engineering and Operations Research at George Mason University, Virginia, U.S.A. His research interests include model-based systems engineering, robust system design, and facility design.
Chun-Hung Chen received his Ph.D. degree from Harvard University in 1994. He is currently a Professor at George Mason University. Dr. Chen was an Assistant Professor at the University of Pennsylvania before joining GMU. He was also a professor at National Taiwan University (Electrical Eng. and Industrial Eng.) from 2008–14. Sponsored by NSF, NIH, DOE, NASA, FAA, Missile Defense Agency, and Air Force in US, he has worked on the development of very efficient methodology for simulation-based decision making and its applications. Dr. Chen received several awards such as “K.D. Tocher Medal” for the best paper in the Journal of Simulation, “National Thousand Talents Award” from China, and Eliahu I. Jury Award from Harvard University. He has served as a Department Editor for IIE Transactions, Department Editor for Asia-Pacific Journal of Operational Research, Associate Editor for IEEE Transactions on Automation Science and Engineering, Associate Editor for IEEE Transactions on Automatic Control, Area Editor for Journal of Simulation Modeling Practice and Theory, Advisory Editor for International Journal of Simulation and Process Modeling, and Advisory Editor for Journal of Traffic and Transportation Engineering. Dr. Chen is the author of two books, including a best seller: “Stochastic Simulation Optimization: An Optimal Computing Budget Allocation”. He is an IEEE Fellow.
Prof. Dr.-Ing. Birgit Vogel-Heuser is a full professor and director of the Institute of Automation and Information Systems at the Technical University of Munich. Her main research interests are systems engineering, software engineering, and modeling of distributed and reliable embedded systems. She is coordinator of the Collaborative Research Centre (CRC) 768: Managing cycles in innovation processes – integrated development of product-service systems based on technical products, member of acatech, chair of the VDI/VDE working group on industrial agents and vice chair of the IFAC TC 3.1 computers in control.
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