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Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Enhancing economic efficiency in modular production systems through deep reinforcement learning
(2024)
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.
Der Forschungskreis Vereinte Nationen veranstaltete am 25. Juni 2016 seine dreizehnte Konferenz in Kooperation mit dem „Forum internationale Ordnung“ des Auswärtigen Amtes. Die Potsdamer UNO-Konferenzen stellen in ihrem Programm traditionell eine Verbindung von Wissenschaft und Praxis her unter Beteiligung unterschiedlicher Disziplinen.
Die Konferenz 2016 widmete sich dem Thema „Die Rolle der Vereinten Nationen in der multilateralen Entwicklungszusammenarbeit“.
Entwicklung als wichtiges Ziel der Vereinten Nationen und ihrer Mitgliedstaaten ist auch für andere Handlungs- und Politikfelder wichtig. Bilanz und Ausblick, die auf der Konferenz unternommen wurden, betrafen deshalb nicht nur die Entwicklungspolitik im eigentlichen Sinne – hier standen die 2015 beschlossenen Nachhaltigkeitsziele (SDGs) zur Debatte –, sondern bezogen auch umweltpolitische und menschenrechtliche Aspekte mit ein.
Die Referate zum Thema Entwicklung wurden eingerahmt von zwei Vorträgen zu aktuellen UN-politischen Fragen: der Wahl des neuen Generalsekretärs der Vereinten Nationen in New York nach einem reformierten Wahlverfahren, das mehr Transparenz und für UN-Mitgliedstaaten und NGOs mehr Beteiligungsmöglichkeiten bietet, und die Neuorganisation innerhalb des Auswärtigen Amtes, was die Vereinten Nationen betrifft.
Background:
Like most countries, Germany is currently recruiting international nurses due to staff shortages. While these are mostly academic, the academisation of nursing in Germany has only just begun. This allows for a broader look at the participation of migrant nurses: How do care teams deal with the fact that immigrant colleagues are theoretically more highly qualified than long-established colleagues?
Methods:
Case studies were conducted in four inpatient care teams of two hospitals in 2022. Qualitative data include 26 observation protocols, 4 group discussions and 17 guided interviews. These were analysed using the documentary method and validated intersubjectively.
Results:
Due to current academisation efforts in Germany and the immigration of academised nursing staff from abroad, the areas of activity and responsibility of nursing in Germany are under negotiating pressure. This concerns basic care for example, which in Germany is provided by skilled workers, but in other countries is mostly provided by assistants or relatives. The question of who should provide basic care, whether all nurses or only nursing assistants, documents the struggle between an established and a new understanding of care. In this context, the knowledge and skills of migrant and academicised care workers become a crucial aspect in the struggle for a new professional identity for care in Germany.
Conclusions:
The specific situation in Germany makes it possible to show the potential for change that international care migration can constitute for destination countries. The far-reaching process of change of German nursing is given a further dimension not only by its academization, but by the immigration of international and academically trained nursing staff, where inclusive or exclusive effects can already be observed.
Key messages: The increasing proportion of migrant nurses accelerates the current discussion on nursing in Germany. Conflict areas show up in everyday work of care teams and must be addressed there.
Einleitung
Pflege in Deutschland befindet sich in einem Veränderungsprozess. Die politisch forcierte Zuwanderung von Pflegekräften sowie die Akademisierung führen zu einem enormen Anpassungsdruck bei allen Beteiligten. Wie wirkt sich dies auf den Arbeitsalltag aus?
Methoden
Die qualitative Datenbasis umfasst bisher 36 Tage Teilnehmende Beobachtung, 17 Themenzentrierte Leitfadeninterviews sowie vier Gruppendiskussionen in vier Pflegeteams zweier Krankenhäuser. Die Analyse erfolgt mit der Dokumentarischen Methode.
Ergebnisse
Am Beispiel der Grundpflege (u. A. dem „Waschen“) wird deutlich, wie die Pflegeteams ihren Arbeitsalltag neu aushandeln. Die Teams mit einer hohen migrationsbezogenen Diversität argumentieren eher, dass die Aufgaben der Grund- und Behandlungspflege entsprechend der Qualifikation als Hilfs- oder Fachkraft erledigt werden sollen. Hier treten stereotype (kulturalisierende) Zuschreibungen in den Hintergrund. Demgegenüber berufen sich Pflegeteams mit einer niedrigen migrationsbezogenen Diversität eher darauf, dass die Grundpflege in Deutschland – anders als in anderen Ländern – zu den Aufgaben einer examinierten Pflegefachkraft zählt. Kolleg*innen aus dem Ausland wird die pflegerische Kompetenz daher eher abgesprochen.
Schlussfolgerung
Die Frage nach der Aufteilung von Grund- und Behandlungspflege, ist auf allen Stationen virulent. Die Teams entwickeln jedoch in Abhängigkeit von ihrer spezifischen Heterogenität unterschiedliche Umgangsweisen. Demzufolge sollte sich Personal- und Organisationsentwicklung insbesondere an den Pflegeteams orientieren.
In honour of Seymour Papert
(2018)
Forth is nice and flexible but to a philosopher and teacher educator Logo is the more impressing language. Both are relatives of Lisp, but Forth has a reverse Polish notation where as Logo has an infix notation. Logo allows top down programming, Forth only bottom up. Logo enables recursive programming, Forth does not. Logo includes turtle graphics, Forth has nothing comparable. So what to do if you can't get Logo and have no information about its inner architecture? This should be a case of "empirical modelling": How can you model observable results of the behaviour of Logo in terms of Forth? The main steps to solve this problem are shown in the first part of the paper.
The second part of the paper discusses the problem of modelling and shows that the modelling of making and the modelling of recognition have the same mathematical structure. So "empirical modelling" can also serve for modelling desired behaviour of technical systems.
The last part of the paper will show that the heuristic potential of a problem which should be modeled is more important than the programming language. The Picasso construal shows, in a very simple way, how children of different ages can model emotional relations in human behaviour with a simple Logo system.