@article{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based production planning and control}, series = {Journal of Manufacturing Systems}, volume = {65}, journal = {Journal of Manufacturing Systems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0278-6125}, doi = {10.1016/j.jmsy.2022.10.019}, pages = {743 -- 766}, year = {2022}, abstract = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.}, language = {en} } @article{PanzerBenderGronau2023, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {A deep reinforcement learning based hyper-heuristic for modular production control}, series = {International journal of production research}, journal = {International journal of production research}, publisher = {Taylor \& Francis}, address = {London}, issn = {0020-7543}, doi = {10.1080/00207543.2023.2233641}, pages = {1 -- 22}, year = {2023}, abstract = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.}, language = {en} } @article{UllrichWeberGronau2023, author = {Ullrich, Andr{\´e} and Weber, Edzard and Gronau, Norbert}, title = {Regionale Refabrikationsnetzwerke}, series = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse}, volume = {39}, journal = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse}, number = {2}, publisher = {GITO mbH Verlag}, address = {Berlin}, issn = {2364-9208}, doi = {10.30844/IM_23-2_11-14}, pages = {11 -- 14}, year = {2023}, abstract = {Die Herstellung von Produkten bindet Energie sowie auch materielle Ressourcen. Viel zu langsam entwickeln sich sowohl das Bewusstsein der Konsumenten sowie der Produzenten als auch gesetzgebende Aktivit{\"a}ten, um zu einem nachhaltigen Umgang mit den zur Verf{\"u}gung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es erm{\"o}glicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu f{\"o}rdern und effiziente L{\"o}sungen f{\"u}r die regionale Wieder- und Weiterverwendung von G{\"u}tern anzubieten.}, language = {de} } @article{BenderKorjahnGronau2024, author = {Bender, Benedict and Korjahn, Nicolas and Gronau, Norbert}, title = {Erfolgreich auf Handelsplattformen}, series = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen}, volume = {20}, journal = {ERP-Management : Auswahl, Einf{\"u}hrung und Betrieb von ERP-Systemen}, number = {1}, publisher = {GITO mbH - Verlag f{\"u}r Industrielle Informationstechnik und Organisation}, address = {Berlin}, issn = {1860-6725}, pages = {76 -- 82}, year = {2024}, abstract = {Obwohl Handelsplattformen zunehmend an Bedeutung gewinnen, besteht im deutschsprachigen Raum ein Mangel an umfassenden Markt{\"u}bersichten. Dadurch fehlt es Verk{\"a}ufern, potenziellen Plattformbetreibern und Kunden an einer soliden Grundlage f{\"u}r fundierte Entscheidungen. Das {\"a}ndern wir mit folgendem Beitrag. Erfahren Sie hier das Wichtigste {\"u}ber den rasant wachsenden Markt der Handelsplattformen.}, language = {de} } @article{PanzerGronau2024, author = {Panzer, Marcel and Gronau, Norbert}, title = {Enhancing economic efficiency in modular production systems through deep reinforcement learning}, series = {Procedia CIRP}, volume = {121}, journal = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2023.09.229}, pages = {55 -- 60}, year = {2024}, abstract = {In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios.}, language = {en} } @article{GrumHiesslMareschetal.2021, author = {Grum, Marcus and Hiessl, Werner and Maresch, Karl and Gronau, Norbert}, title = {Design of a neuronal training modeling language}, series = {AIS-Transactions on enterprise systems}, volume = {5}, journal = {AIS-Transactions on enterprise systems}, number = {1}, publisher = {GITO-Publ., Verl. f{\"u}r Industrielle Informationstechnik und Organisation}, address = {Berlin}, issn = {1867-7134}, doi = {10.30844/aistes.v5i1.20}, pages = {16}, year = {2021}, abstract = {As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.}, language = {en} } @article{VladovaGronau2022, author = {Vladova, Gergana and Gronau, Norbert}, title = {KI-basierte Assistenzsysteme in betrieblichen Lernprozessen}, series = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse}, volume = {38}, journal = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse}, number = {2}, publisher = {GITO mbH Verlag f{\"u}r Industrielle Informationstechnik und Organisation}, address = {Berlin}, issn = {2364-9216}, doi = {10.30844/I40M_22-2_11-14}, pages = {11 -- 14}, year = {2022}, abstract = {Assistenzsysteme finden im Kontext der digitalen Transformation immer mehr Einsatz. Sie k{\"o}nnen Besch{\"a}ftigte in industriellen Produktionsprozessen sowohl in der Anlern- als auch in der aktiven Arbeitsphase unterst{\"u}tzen. Kompetenzen k{\"o}nnen so arbeitsplatz- und prozessnah sowie bedarfsorientiert aufgebaut werden. In diesem Beitrag wird der aktuelle Forschungsstand zu den Einsatzm{\"o}glichkeiten dieser Assistenzsysteme diskutiert und mit Beispielen illustriert. Es werden unter anderem auch Herausforderungen f{\"u}r den Einsatz aufgezeigt. Am Ende des Beitrags werden Potenziale f{\"u}r die zuk{\"u}nftige Nutzung von AS in industriellen Lernprozessen und f{\"u}r die Forschung identifiziert.}, language = {de} } @article{KlippertStolpmannGrumetal.2023, author = {Klippert, Monika and Stolpmann, Robert and Grum, Marcus and Thim, Christof and Gronau, Norbert and Albers, Albert}, title = {Knowledge transfer quality improvement}, series = {Procedia CIRP}, volume = {119}, journal = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2023.02.171}, pages = {919 -- 925}, year = {2023}, abstract = {Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.}, language = {en} } @article{UllrichTeichmannGronau2021, author = {Ullrich, Andr{\´e} and Teichmann, Malte and Gronau, Norbert}, title = {Fast trainable capabilities in software engineering-skill development in learning factories}, series = {Ji suan ji jiao yu = Computer Education / Qing hua da xue}, journal = {Ji suan ji jiao yu = Computer Education / Qing hua da xue}, number = {12}, publisher = {[Verlag nicht ermittelbar]}, address = {Bei jing shi}, issn = {1672-5913}, doi = {10.16512/j.cnki.jsjjy.2020.12.002}, pages = {2 -- 10}, year = {2021}, abstract = {The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities. Accordingly, an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods. We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities. This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach, which enables non-computer scientists to code. Therein, the learning content is provided in small tasks and embedded in learning factory scenarios. Therefore, different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated, yet condensed skills catalogue. This enables the development of training and education units that focus on the most important skills demanded on the market. To achieve this objective, individual learning scenarios are developed. Of course, proper basic skills in coding cannot be learned over night but software programming is also no sorcery.}, language = {en} } @article{ThimUllrichEigelshovenetal.2020, author = {Thim, Christof and Ullrich, Andr{\´e} and Eigelshoven, Felix and Gronau, Norbert and Ritter, Ann-Carolin}, title = {Crowdsourcing bei industriellen Innovationen}, series = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse}, volume = {36}, journal = {Industrie 4.0 Management : Gegenwart und Zukunft industrieller Gesch{\"a}ftsprozesse}, number = {6}, publisher = {GITO mbH Verlag}, address = {Berlin}, issn = {2364-9208}, doi = {10.30844/I40M_20-6_S9-13}, pages = {9 -- 13}, year = {2020}, abstract = {Die Innovationst{\"a}tigkeit im industriellen Umfeld verlagert sich durch die Digitalisierung hin zu Produkt-Service-Systemen. Kleine und mittlere Unternehmen haben sich in ihrer Entwicklungst{\"a}tigkeit bisher stark auf die Produktentwicklung bezogen. Der Umstieg auf „smarte" Produkte und die Kopplung an Dienstleistungen erfordert h{\"a}ufig personelle und finanzielle Ressourcen, welche KMU nicht aufbringen k{\"o}nnen. Crowdsourcing stellt eine M{\"o}glichkeit dar, den Innovationsprozess f{\"u}r externe Akteure zu {\"o}ffnen und Kosten- sowie Geschwindigkeitsvorteile zu realisieren. Bei der Integration von Crowdsourcing-Elementen ist jedoch einigen Herausforderungen zu begegnen. Dieser Beitrag zeigt sowohl die Potenziale als auch die Barrieren einer Crowdsourcing-Nutzung im industriellen Umfeld auf.}, language = {de} }