@misc{13675,
  abstract     = {{This paper extends a previously developed biobjective Mixed-Integer Linear Programming (MILP) methodology for reducing electricity costs and CO2 emissions in Smart-E-Factory applications. While the earlier approach assumed fixed photovoltaic (PV) and battery capacities, we now propose a cascaded optimization framework to determine the optimal sizing (power rating and orientation of the PV system, battery capacity) while simultaneously optimizing battery dispatch. The cost function combines operational costs with amortized investment costs of both PV and battery systems, embedded in a dynamic scheduling optimization that addresses real-time electricity price and CO2 signals. Numerical results indicate that intermediate capacities and balanced east/west orientation maximize cost-effectiveness and emission reductions. This study underscores the value of coupling parametric design and dispatch optimization to achieve scalable, sustainable solutions for industrial energy systems.}},
  author       = {{Mousavi, Seyed Davood and Schulte, Thomas}},
  booktitle    = {{	 6th International Conference on Electrical, Communication and Computer Engineering (ICECCE 2025) : 27-28 August 2025, Istanbul, Türkiye}},
  isbn         = {{979-8-3315-4915-2 }},
  keywords     = {{Photovoltaic systems, Cost, Electricity, Tariffs, Stochastic processes, Real-time systems, Robustness, Batteries, Planning, Mixed integer linear programming}},
  location     = {{Istanbul, Turkiye }},
  publisher    = {{IEEE}},
  title        = {{{Cascaded Optimization of PV and Battery Sizing Under Dynamic Cost and CO Signals}}},
  doi          = {{10.1109/icecce67514.2025.11257982}},
  year         = {{2025}},
}

@misc{10935,
  abstract     = {{One of the challenges in universities is to take into account the different competences of students throughout the process of imparting knowledge. It is unlikely that all students will grasp the course content in the same way, as they have different abilities. Similarly, it is unrealistic for teachers to address all the individual needs of each student at all times. In addition, maintaining learners' attention to a particular topic is also an essential part of teaching. To overcome these challenges, we are developing AI learning software as part of a research project. The software generates learning scenarios for the students and takes their individual competences into account. The research project focuses on the field of automation technology, especially on programming, as programming skills are usually required in automation environments. When solving the scenarios, students receive immediate feedback on how well they have solved the task. Immediate automated feedback not only meets the students' expectations of quick learning feedback, but it also relieves the teaching staff in the assessment process. In addition, the software includes an interactive user interface that allows learners to see the results of their programming tasks in a simulated 3D environment. This approach aims to maintain learners' attention over a longer learning period. This paper describes the above aspects that are being developed in the research project and previews the new learning opportunities that could arise from the use of AI.}},
  author       = {{Ali, Asmar and Deuter, Andreas}},
  booktitle    = {{Journal of international scientific publications / Science & Education Foundation : Educational Alternatives }},
  issn         = {{1313-2571}},
  keywords     = {{ai education, automated feedback, automation education, educational software, programming}},
  location     = {{Burgas}},
  pages        = {{12--20}},
  publisher    = {{Info Invest }},
  title        = {{{An AI Assistant for Education in Automation}}},
  doi          = {{10.62991/EA1996108906}},
  volume       = {{21}},
  year         = {{2023}},
}

@inbook{6917,
  abstract     = {{The Industrial Engineering Laboratory at the Ostwestfalen-Lippe University of Applied Sciences and Arts researches the user-centered, customer-oriented and efficient design of work and production systems. Its research focuses on investigating different technologies from the context of digitalization in industrial production. Software used to digitally support work processes must be adapted specifically to work systems. It is difficult to take various user requirements into account in standard software. Therefore, IT experts must continuously adapt software in order to make it suitable for different applications. One possible alternative is for software applications to be designed by industrial engineering or users themselves. In low-code programming, in contrast to classic software development, it is possible to create software applications without extensive programming knowledge. In the laboratory, a teaching unit on app development using a low-code platform was designed. It was integrated into an existing teaching concept for industrial engineers, then evaluated using a questionnaire.}},
  author       = {{Adrian, Benjamin and Hinrichsen, Sven and Nikolenko, Alexander}},
  booktitle    = {{Advances in Intelligent Systems and Computing}},
  editor       = {{Nunes, I.}},
  isbn         = {{9783030513689}},
  issn         = {{2194-5357}},
  keywords     = {{Cognitive assistance systems, Low-code programming, Didactics}},
  pages        = {{45--51}},
  publisher    = {{Springer}},
  title        = {{{App Development via Low-Code Programming as Part of Modern Industrial Engineering Education}}},
  doi          = {{10.1007/978-3-030-51369-6_7}},
  volume       = {{1207}},
  year         = {{2020}},
}