Institut für Management und Technik
Refine
Year of publication
Document Type
- Article (108)
- Book (40)
- Part of a Book (30)
- Conference Proceeding (21)
- Bachelor Thesis (3)
- Report (3)
- Doctoral Thesis (2)
- Working Paper (2)
- Course Material (1)
- Master's Thesis (1)
Keywords
- Logistik (6)
- logistics (4)
- Ablaufplanung (2)
- Anti-Wachstumsthese (2)
- Arbeitsteilungsfalle (2)
- IFRS (2)
- Logistikdilemma (2)
- logistic (2)
- thesis of anti growth (2)
- Acceptance (1)
Institute
- Institut für Management und Technik (212)
- Fakultät WiSo (4)
- Fakultät AuL (2)
- Fakultät IuI (2)
- Institut für Duale Studiengänge (2)
- Fakultät MKT (1)
- LearningCenter (1)
Sustainability Research 2023
(2024)
Analyse von Erdgas- und Stromlastprofilen für die Dekarbonisierungsstrategie eines Gewerbegebiets
(2025)
Ziel des Beitrags ist es, eine Datengrundlage zu schaffen, die den unternehmensspezifischen Energiebedarf eines Gewerbegebiets in stündlicher Auflösung abbildet. Im Fokus steht die Erhebung des Strom- und Wärmebedarfsprofils von Unternehmen eines Gewerbegebiets mittels einer Kombination aus synthetischen und realen Lastprofilen. Diese werden benötigt, um die heterogenen Energiebedarfe von Industrie- und GHD-Unternehmen in einem Gewerbegebiet möglichst realitätsnah abzubilden. Aufgrund der eingeschränkten Verfügbarkeit realer Verbrauchsdaten werden 323 synthetische Strom- und 125 Gaslastprofile aus verschiedenen Studien herangezogen. Der Vergleich zeigt, dass synthetische Profile die tatsächlichen Bedarfe einzelner Unternehmen nur eingeschränkt wiedergeben können. Jedoch nähern sich die synthetischen Daten mit zunehmender Aggregation dem realen Verbrauchsprofil des gesamten Gewerbegebiets an. Die durchgeführte Erhebung und Analyse bilden die Grundlage zur Implementierung eines Energiesystemmodells, das die ökonomischen und technischen Synergien lokaler Energiegemeinschaften im Rahmen von Dekarbonisierungsstrategien in Gewerbegebieten untersucht.
Sustainability sponsorship
(2024)
Der AI Act
(2024)
The significance of digital technologies in the context of digitizing production processes, such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of research is the optimization of digital twins through Reinforcement Learning (RL). This necessitates a simulation environment that can be integrated with RL. One is introduced in this paper as the Digital Model Playground (DMPG). The paper outlines the implementation of the DMPG, followed by demonstrating its application in optimizing production scheduling through RL within a sample process. Although there is potential for further development, the DMPG already enables the modeling and optimization of production processes using RL and is comparable to commercial discrete event simulation software regarding the simulation-speed. Furthermore, it is highly flexible and adaptable, as shown by two projects, which distribute the DMPG to a high-performance cluster or generate 2D/3D-Visualization of the simulation model with Unreal. This establishes the DMPG as a valuable tool for advancing the digital transformation of manufacturing systems, affirming its potential impact on the future of production optimization. Currently, planned extensions include the integration of more optimization algorithms and Process Mining techniques, to further enhance the usability of the framework.
In this research work, the calculation of carbon dioxide emissions for trailer traffic based on sensor data is described. The data consist of trailer type, load, trip and route parameters. While the individual trailer parameters can be derived from the order management, the trip and route parameters are collected by frequently data. With this, the transport carbon footprint is calculated based on DIN EN 16258 by means of the ProBas database. The distributions of carbon dioxide emissions in trailer traffic are analysed and discussed. The results of the case study indicate that sensor-based modelling can be a useful tool for an improved transparency and estimation of carbon dioxide emissions.
Enhancing Digital Twins for Production through Process Mining Techniques : A Literature Review
(2023)
A digital twin (DT) plays a vital role in the advancement of manufacturers towards Industry 4.0. However, the creation and maintenance of DTs can be time-consuming. One approach to streamline this process is the utilization of process mining (PM) methods and techniques, which can automatically generate valuable information for DTs. Therefore, this paper aims to examine different approaches that augment DTs with PM and explore their effects. The review categorizes these approaches into three groups: theoretical approaches, approaches with laboratory case studies, and approaches with real-world case studies conducted by manufacturers. The review reveals that the use of PM can enhance the flexibility and sustainability of DTs. However, this improvement comes at the cost of requiring high-quality data and more data preparation efforts.
Action recognition technology has gained significant traction in recent years. This paper focuses on evaluating neural network architectures for action recognition in the production industry. By utilizing datasets tailored for production or assembly tasks, various architectures are assessed for their accuracy and performance. The findings of this study provide some insights and guidance for researchers and practitioners to select an appropriate architecture or pretrained models for action recognition in the production industry.