TY - CHAP A1 - Schmid, Florian A1 - Wild, Tobias A1 - Schneidewind, Jan A1 - Vogl, Tobias A1 - Schuhegger, Lukas A1 - Galka, Stefan T1 - Simulation Based Approach for Reconfiguration and Ramp up Scenario Analysis in Factory Planning T2 - Proceedings of the 2022 Winter Simulation Conference (WSG), 11-14 Dec. 2022, Singapore N2 - Structural changes in production entail a potential economic risk for manufacturing companies. It is necessary to identify a suitable strategy for the reconfiguration process and to continue to meet the demand during the change in the factory structure and ramp-up phase. A simulation offers the possibility to analyze different ramp-up scenarios for the factory structure and to select a suitable concept for the reconfiguration process. A discrete event simulation approach is presented that can be used to evaluate variants of structural changes and serves as a basis for deciding on a reconfiguration strategy. This approach is demonstrated using a specific production step of a plant producing hydrogen electrolyzers, the results and generalized conclusions are discussed. KW - Simulation KW - Production KW - Ramp up Y1 - 2022 SN - 978-1-6654-7661-4 U6 - https://doi.org/10.1109/WSC57314.2022.10015310 SP - 2842 EP - 2852 PB - IEEE ER - TY - CHAP A1 - Galka, Stefan A1 - Schuhegger, Lukas ED - Feng, B. ED - Pedrielli, G. ED - Peng, Y. ED - Shashaani, S. ED - Song, E. ED - Corlu, C.G. ED - Lee, L.H. ED - Chew, E.P. ED - Roeder, T. ED - Lendermann, P. T1 - Integration of Discrete-event Simulation in the Planning of a Hydrogen Electrolyzer Production Facility T2 - Proceedings of the 2022 Winter Simulation Conference (WSC), 11-14 Dec. 2022, Singapore N2 - In the context of production and factory planning, the expansion of the factory must already be taken into account during initial planning. This results in an increase in planning complexity, as the involved planners have to know the expansion stages of the factory in the different time periods and have to evaluate concept modifications across all time periods. This paper presents an idea for a planning tool, which takes expansion stages into consideration. The data model contains all relevant information to generate a simulation model of the factory in an almost automated way. The aim is to enable factory planners to quickly investigate concept changes with the help of simulation, for example, to identify bottlenecks. KW - Simulation KW - Production KW - Digital Twin Y1 - 2022 UR - https://informs-sim.org/wsc22papers/pos114.pdf PB - IEEE CY - Singapore ER - TY - CHAP A1 - Ulbrich, Alexander A1 - Galka, Stefan A1 - Günthner, Willibald A. T1 - Secure planning of order picking systems with the aid of simulation T2 - Proceedings of th 43rd Hawaii International Conference on System Sciences (HICSS), 5-8 Jan. 2010, Koloa, Kauai, Hawaii N2 - Planning a new logistics center is a quite difficult job. Depending on the requirements there are a great number of possible design versions. The planner has to consider not only the present but also the future requirements. Up to now, the planner decides on the basis of experience which system will perform the requested services best. This paper illustrates the redesign of a new order-picking system in industry supported by a simulation tool of our institute designed especially for the rough planning. With the described project the road capability of the applied simulation concept is underlined. Several versions based on the requirements were investigated and evaluated. Key figures are calculated and a recommendation depending on performance and arising expenses is given. Y1 - 2010 SN - 978-0-7695-3869-3 SN - 076953869X U6 - https://doi.org/10.1109/HICSS.2010.326 SN - 1530-1605 SP - 1 EP - 7 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Schneidewind, Jan A1 - Galka, Stefan T1 - Interactive reinforcement learning-based factory layout planning T2 - 13th Conference on Learning Factories (CLF 2023), 9-11 May 2023, Reutlingen, Germany N2 - Due to the complexity and the number of factors involved in factory layout planning, computers were identified as an efficient tool to support the process. However, so far no method for computer-aided layout planning has gained wide acceptance in practical application. One reason for this is that in present approaches either the user or the computer designs the layout, neglecting either the qualitative or the quantitative goals. To bridge this gap, this article introduces a concept for human-computer-integration based on evaluative feedback and inverse reinforcement learning. A key element of the concept is the interactive planning process in which user and computer alternately design and improve the layout until a satisfactory layout is found. The user evaluates the layouts according to qualitative criteria, adjusts them intuitively and specifies objectives and restrictions in an explorative way. The computer on the other hand – in form of a reinforcement algorithm – generates possible layouts and incorporates the user’s feedback into its policy. This synergy is expected to generate better results than an expert or an algorithm alone could. Furthermore, in the context of learning factories, it encourages critical thinking and allows students to develop a deeper understanding of the factors that contribute to efficient manufacturing processes. Both an architecture for the implementation is proposed and the requirements for the user interface are specified. KW - factory planning KW - facility layout problem KW - machine learning KW - optimization KW - inverse reinforcement learning Y1 - 2023 UR - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4469300 PB - SSRN ER - TY - GEN A1 - Galka, Stefan T1 - Podcast "Irgendwas mit Logistik", Folge 82: Automatisierungstechnik richtig und fair abnehmen N2 - Professoren wird häufig unterstellt Theoretiker zu sein. Das haben wir zum Anlass genommen, um gemeinsam mit Stefan Galka, Professor an der OTH Regensburg, im Irgendwas mit Logistik Podcast über ein ziemlich praktisches Thema zu sprechen - Inbetriebnahmen von Automatisierungstechnik. Stefan, Jens und Thomas zäumen das Pferd von hinten auf und versuchen zu klären, wer welche Verantwortungen und (ggf. verdeckten) Interessen bei der Realisierung von technischen Gewerken hat, welche Richtlinien bei der Durchführung helfen können und definieren gemeinsam, wie die Inbetriebnahme zum Erfolg wird. Y1 - 2021 UR - https://irgendwas-mit-logistik.podigee.io/82-automatisierungstechnik-richtig-und-fair-abnehmen-gast-stefan-galka-von-der-oth-regensburg ET - veröffentlicht am 05.04.2021 ER - TY - CHAP A1 - Galka, Stefan A1 - Scherbarth, Christoph ED - Franke, Jörg ED - Schuderer, Peter T1 - Simulationsbasierte Untersuchung der Grenzproduktivität von Robotern in einem AutoStore-Lagersystem T2 - Simulation in Produktion und Logistik 2021 : Erlangen, 15.-17. September 2021 Y1 - 2021 SN - 978-3-7369-7479-1 SP - 197 EP - 206 PB - Cuvillier Verlag CY - Göttingen ER - TY - BOOK A1 - Galka, Stefan T1 - AutoStore - was Nutzer über das System berichten können BT - Ergebnisse einer Online-Umfrage KW - Kleinteil KW - Lager KW - Intralogistik KW - Umfrage Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-6463 SN - 978-3-9818209-9-7 CY - Regensburg ER - TY - CHAP A1 - Strassburger, Steffen A1 - Galka, Stefan A1 - Lang, Sebastian A1 - Reggelin, Tobias ED - Bui, Tung T1 - Introduction to the Minitrack on Simulation Modeling, Artificial Intelligence and Digital Twins for Decision Making in Production and Logistics T2 - Proceedings of the 58th Hawaii International Conference on System Sciences, January 7-10, 2025, Honolulu, Hawaii N2 - The minitrack contains contributions with a focus on simulation modeling and digital twins and decision making in the context of Industry 4.0. The use of simulation models and digital twins is manifold, from planning to virtual commission and real-time operational decision support. Cyber-physical systems integrate the real world and the virtual world to enable decision making in the age of Industry 4.0. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions. Thus, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Methods include discrete-event simulation, discrete-rate simulation, hybrid simulation, system dynamics simulation, the combination of simulation modeling with machine learning or optimization heuristics, prescriptive analytics, and adaptive systems. Furthermore, this minitrack addresses simulation education and simulation models and digital twins used for education and training in manufacturing and logistics. Y1 - 2025 SN - 978-0-9981331-8-8 U6 - https://doi.org/10.24251/HICSS.2025.205 SP - 1677 EP - 1678 PB - University of Hawaii at Manoa ER - TY - GEN A1 - Wild, Tobias A1 - Galka, Stefan A1 - Wappler, Mona A1 - Xing, Lu ED - Potter, A. ED - Pawar, K. S. ED - O’Reilly, S. ED - Jimo, A. T1 - Integrating supply chain aspects into factory planning, a simulation approach T2 - The journey to sustainable supply chains: proceedings of the 26th International Symposium on Logistics (ISL 2022), 10-13th July 2022, Cork, Ireland N2 - One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials. KW - factory planning KW - supply chain management KW - replenishment strategies KW - space requirements KW - discrete event simulation Y1 - 2022 UR - https://isl21.org/wp-content/uploads/2022/07/ISL_2022_Proceedings.pdf SN - 978-0-85358-350-9 PB - Nottingham University Business School, UK CY - Nottingham, UK ER - TY - CHAP A1 - Galka, Stefan T1 - Untersuchung von Auftragsfreigabestrategien für mehrzonige Kommissioniersysteme im Online-Lebensmittelhandel T2 - ASIM SST 2022 Proceedings Langbeiträge, 26. ASIM Symposium Simulationstechnik: 25.07.-27.07.2022, TU Wien N2 - This paper briefly presents the challenges for order control and release of multi-zone order picking systems. On the one hand, the order control must ensure that all orders are processed on time, and on the other hand, the space requirements (buffer) and the utilisation of the zones must be considered. Within the framework of a case study, different strategies for order release were developed. The paper shortly describes the ideas of the strategies and presents results of a case-based simulative evaluation of the strategies. The findings of the simulation study are the basis for the development of a digital twin for the operational control of multi-zone picking systems. KW - Simulation KW - Kommissionierung KW - Simulation KW - Logistik KW - Planung Y1 - 2022 SN - 978-3-901608-97-1 U6 - https://doi.org/10.11128/arep.20.a2021 VL - 2022 SP - 23 EP - 30 PB - ARGESIM Verlag CY - Wien ER - TY - CHAP A1 - Galka, Stefan A1 - Günthner, Willibald A. A1 - Ulbrich, Alexander T1 - Planning of order-picking-systems with support of discrete simulation T2 - Logistics and Supply Chain Management: Deutsch-Russische Perspektiven. Proceedings of the 5th German-Russian Logistics Workshop (DR-LOG10) Y1 - 2010 ER - TY - JOUR A1 - Galka, Stefan A1 - Klenk, Eva Marianne A1 - Günthner, Willibald A. T1 - Einfluss von Artikeleigenschaften auf die Entnahmezeit in Kommissioniersystemen JF - Logistics Journal / Proceedings N2 - Das Kommissionieren zählt zu den arbeitsintensivsten Aufgaben in der Logistik. Aus diesem Grund ist es wichtig, möglichst genau die erwarteten Prozesszeiten für diese Aufgabe zu bestimmen. Ein verbreiteter Ansatz für die Bestimmung von Prozesszeiten für manuelle Tätigkeiten beim Kommissionieren ist die Methods-Time-Measurement (MTM). Um eine Vorgabezeit mittels MTM zu bestimmen, müssen Einflussfaktoren spezifiziert werden. Dieser Beitrag zeigt anhand der Greifzeit auf, wie diese Einfluss-faktoren auf Grundlage von Artikelstammdaten bestimmt werden können. Weiterhin wird der Unterschied zwischen einer für jeden Artikel individuell bestimmten Greifzeit und der Greifzeit basierend auf repräsentativen Eigenschaften von Artikelgruppen verglichen. Wesentlich wird dabei die mittels MTM bestimmte Greifzeit durch das Gewicht und die Abmessungen (Sperrigkeit) beeinflusst. T2 - Impact of items characteristics on picking time in order picking systems systems Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0009-14-44540 SN - 2192-9084 PB - WGTL, Wissenschaftliche Gesellschaft für Technische Logistik e. V. CY - Stuttgart ; Dortmund ER - TY - JOUR A1 - Galka, Stefan A1 - Rammelmeier, Tobias A1 - Günthner, Willibald A. T1 - Fehlervermeidung in der kommissionierung JF - Logistics Journal N2 - Manuelle Kommissioniersysteme sind aufgrund ihrer Flexibilität weit verbreitet, jedoch treten bei diesen auch vermehrt Kommissionierfehler auf, die weitreichende Folgen haben können. Deshalb werden zu Beginn des Beitrags die Auswirkungen und Ursachen von Kommissionierfehlern betrachtet. Der Fokus des Beitrags liegt auf der Darlegung von Möglichkeiten zur Fehlervermeidung. Dabei wird ein ganzheitlicher Ansatz verfolgt, der Maßnahmen aus den Handlungsfeldern Technik, Prozess, Organisation und Motivation umfasst. Als Ausblick auf zukünftige technische Hilfsmittel zur Fehlervermeidung wird das Augmented-Reality-unterstützte System Pick-by-Vision aus der Forschung vorgestellt, das wesentliche Vorteile existierender Techniken der Informationsbereitstellung vereinigt. T2 - Error prevention in manual order picking KW - Kommissionierfehlervermeidung KW - Kommissionierung KW - Pick-by-Vision KW - augmented reality KW - order picking KW - picking error Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0009-14-34494 SN - 2192-9084 N1 - https://www.logistics-journal.de/proceedings/2012/3449 Jedermann darf dieses Werk unter den Bedingungen der freien Digital Peer Publishing Lizenz elektronisch über­mitteln und zum Download bereit­stellen. Der Lizenztext ist im Internet unter der Adresse http://www.dipp.nrw.de/lizenzen/dppl/fdppl/f-DPPL_v1_de_11-2004.html abrufbar. SP - 1 EP - 8 PB - WGTL, Wissenschaftliche Gesellschaft für Technische Logistik e. V. CY - Stuttgart ; Dortmund ER - TY - CHAP A1 - Reggelin, Tobias A1 - Galka, Stefan A1 - Ivanov, Dmitry A1 - Lang, Sebastian T1 - Introduction to the Minitrack on Modeling and Decision Making in Manufacturing and Logistics in the Age of Industry 4.0 T2 - Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS): 2021 Hawaii, USA N2 - The minitrack contains contributions with a focus on modeling and decision making in manufacturing and logistics in the context of Industry 4.0. Cyber-physical systems integrate the real world and the virtual world. For example, customer decisions and supply disruptions which directly influence manufacturing and logistics systems require immediate decisions in terms of planning and controlling the manufacturing and logistics systems. In order to immediately respond to these dynamics, models must have the capability to support decision making in manufacturing as well as in internal and external logistics in real-time. Two of the Mintrack papers describe concepts and models for digital twins. One paper provides an adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems and one paper investigates matrix production systems and with their requirements and impacts on logistics planning. Y1 - 2021 SN - 978-0-9981331-4-0 U6 - https://doi.org/10.24251/HICSS.2021.198 SP - 1643 EP - 1644 PB - University of Hawai'i at Manoa Hamilton Library CY - Honolulu, HI ER -