@misc{PanzerBenderGronau2021, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Deep reinforcement learning in production planning and control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {2701-6277}, doi = {10.25932/publishup-60572}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605722}, pages = {13}, year = {2021}, abstract = {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 reinforcement 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 sensorand 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.}, language = {en} } @misc{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based production planning and control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-60477}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604777}, pages = {26}, 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} } @misc{PanzerBenderGronau2023, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {A deep reinforcement learning based hyper-heuristic for modular production control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-60564}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605642}, pages = {24}, 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} } @misc{BenderHabibGronau2020, author = {Bender, Benedict and Habib, Natalie and Gronau, Norbert}, title = {Digitale Plattformen}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {1}, issn = {1867-5808}, doi = {10.25932/publishup-60541}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605419}, pages = {11}, year = {2020}, abstract = {Obwohl digitale Plattformen vornehmlich von Großunternehmen betrieben werden, bieten sie klein- und mittelst{\"a}ndischen Unternehmen (KMU) Potenziale zur Verbreitung innovativer Technologien und f{\"u}r den Ausbau ihres Gesch{\"a}ftsmodells. F{\"u}r die Umsetzung digitaler Plattformen stehen Unternehmen mehrere Strategien zur Verf{\"u}gung. Der Beitrag vergleicht und bewertet grundlegende Strategien am Beispiel eines Maschinenbauunternehmens. Die Ergebnisse dienen als Grundlage f{\"u}r die Entscheidungsfindung von KMU.}, language = {de} } @misc{UllrichWeberGronau2023, author = {Ullrich, Andr{\´e} and Weber, Edzard and Gronau, Norbert}, title = {Regionale Refabrikationsnetzwerke}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {2}, issn = {2364-9208}, doi = {10.25932/publishup-60451}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604510}, pages = {6}, 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} } @misc{BenderGronau2022, author = {Bender, Benedict and Gronau, Norbert}, title = {Introduction to the Minitrack on towards the future of enterprise systems}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, editor = {Bui, Tung}, isbn = {978-0-9981331-5-7}, issn = {1867-5808}, doi = {10.25932/publishup-60540}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605406}, pages = {4}, year = {2022}, abstract = {Enterprise systems have long played an important role in businesses of various sizes. With the increasing complexity of today's business relationships, pecialized application systems are being used more and more. Moreover, emerging technologies such as artificial intelligence are becoming accessible for enterprise systems. This raises the question of the future role of enterprise systems. This minitrack covers novel ideas that contribute to and shape the future role of enterprise systems with five contributions.}, language = {en} } @misc{VladovaUllrichBenderetal.2021, author = {Vladova, Gergana and Ullrich, Andr{\´e} and Bender, Benedict and Gronau, Norbert}, title = {Students' Acceptance of Technology-Mediated Teaching - How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany}, series = {Postprints der Universit{\"a}t Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-52161}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-521615}, pages = {17}, year = {2021}, abstract = {In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring-summer 2020 semester. Our study focused on (1) the students' acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study's results and derive short- and long-term implications for science and practice.}, language = {en} } @misc{WeberTiefenbacherGronau2019, author = {Weber, Edzard and Tiefenbacher, Anselm and Gronau, Norbert}, title = {Need for standardization and systematization of test data for job-shop scheduling}, series = {Postprints der Universit{\"a}t Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {134}, issn = {1867-5808}, doi = {10.25932/publishup-47222}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472229}, pages = {23}, year = {2019}, abstract = {The development of new and better optimization and approximation methods for Job Shop Scheduling Problems (JSP) uses simulations to compare their performance. The test data required for this has an uncertain influence on the simulation results, because the feasable search space can be changed drastically by small variations of the initial problem model. Methods could benefit from this to varying degrees. This speaks in favor of defining standardized and reusable test data for JSP problem classes, which in turn requires a systematic describability of the test data in order to be able to compile problem adequate data sets. This article looks at the test data used for comparing methods by literature review. It also shows how and why the differences in test data have to be taken into account. From this, corresponding challenges are derived which the management of test data must face in the context of JSP research. Keywords}, language = {en} } @misc{KlugeGronau2018, author = {Kluge, Annette and Gronau, Norbert}, title = {Intentional forgetting in organizations}, series = {Postprints der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {127}, issn = {1867-5808}, doi = {10.25932/publishup-44602}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-446022}, pages = {19}, year = {2018}, abstract = {To cope with the already large, and ever increasing, amount of information stored in organizational memory, "forgetting," as an important human memory process, might be transferred to the organizational context. Especially in intentionally planned change processes (e.g., change management), forgetting is an important precondition to impede the recall of obsolete routines and adapt to new strategic objectives accompanied by new organizational routines. We first comprehensively review the literature on the need for organizational forgetting and particularly on accidental vs. intentional forgetting. We discuss the current state of the art of theory and empirical evidence on forgetting from cognitive psychology in order to infer mechanisms applicable to the organizational context. In this respect, we emphasize retrieval theories and the relevance of retrieval cues important for forgetting. Subsequently, we transfer the empirical evidence that the elimination of retrieval cues leads to faster forgetting to the forgetting of organizational routines, as routines are part of organizational memory. We then propose a classification of cues (context, sensory, business process-related cues) that are relevant in the forgetting of routines, and discuss a meta-cue called the "situational strength" cue, which is relevant if cues of an old and a new routine are present simultaneously. Based on the classification as business process-related cues (information, team, task, object cues), we propose mechanisms to accelerate forgetting by eliminating specific cues based on the empirical and theoretical state of the art. We conclude that in intentional organizational change processes, the elimination of cues to accelerate forgetting should be used in change management practices.}, language = {en} } @misc{Gronau2005, author = {Gronau, Norbert}, title = {Ermittlung der Zukunftsf{\"a}higkeit unternehmensweiter Anwendungssysteme}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-6843}, year = {2005}, abstract = {Bei Entscheidungen {\"u}ber abzul{\"o}sende oder neue Anwendungssysteme kann mit Hilfe funktionaler Anforderungen immer nur der gegenw{\"a}rtige oder vorhersehbare Bedarf ermittelt werden. In einem turbulenten Umfeld sind die Anwendungssysteme jedoch h{\"a}ufig langere Zeit im Einsatz als die Anforderungen g{\"u}ltig sind, mit Hilfe derer sie ausgew{\"a}hlt wurden. An der Universit{\"a}t Potsdam wird im Rahmen des BMBF-Projektes CHANGE eine Vorgehensweise zur Ermittlung der Zukunftsf{\"a}higkeit unternehmensweiter Anwendungssysteme entwickelt, deren wesentliche Merkmale in diesem Beitrag beschrieben werden.}, subject = {Enterprise-Resource-Planning}, language = {de} }