Wetzel, S., & Bertel, S. (2018). Extraction of Time Dependent Physical Rotation Strategies. 14th biannual conference of the German Society for Cognitive Science, GK. Tübingen. Germany.
Demirsoy, A., & Petersen, K. (2018). Semantic Knowledge Management System to Support Software Engineers: Implementation and Static Evaluation through Interviews at Ericsson. e-Informatica Software Engineering Journal, 12.
Wallbaum, T., Matviienko, A., Ananthanarayan, S., Olsson, T., Heuten, W., & Boll, S. C. (2018). Supporting communication between grandparents and grandchildren through tangible storytelling systems. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (S. 1–12).
Brandenburg, M., & Hahn, G. J. (2018). Sustainable aggregate production planning in the chemical process industry - A benchmark problem and dataset. Data in Brief, 18, 961–967. http://doi.org/10.1016/j.dib.2018.03.064
Abstract
Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). The need for powerful and efficient approaches to solve complex APP problems persists. Problem-specific solution approaches are advantageous compared to standardized approaches that are designed to provide basic decision support for a broad range of planning problems but inadequate to optimize under consideration of specific settings. This in turn calls for methods to compare different approaches regarding their computational performance and solution quality. In this paper, we present a benchmarking problem for APP in the chemical process industry. The presented problem focuses on (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates and (ii) integrated campaign planning with production mix/volume on the operational level. The mutual trade-offs between economic, environmental and social factors can be considered as externalized factors (production-related carbon emission and overtime working hours) as well as internalized ones (resulting costs). We provide data for all problem parameters in addition to a detailed verbal problem statement. We refer to Hahn and Brandenburg [1] for a first numerical analysis based on and for future research perspectives arising from this benchmarking problem.
Ullmann, N., & Cordts, S. (2018). Empfehlungssysteme für die Produktkonfiguration mit Methoden des maschinellen Lernens. (B. u.a., Hrsg.) (Tagungsband zur 31. AKWI-Jahrestagung). mana-Buch Verlag.
Papatheocharous, E., Wnuk, K., Petersen, K., Sentilles, S. everine, Cicchetti, A., Gorschek, T., & Shah, S. M. A. (2018). The GRADE taxonomy for supporting decision-making of asset selection in software-intensive system development. Information and Software Technology, 100, 1–17.
Löhlein, B., & Huth, G. (2017). Alternative materials for PM synchronous motors. In Innovative Small Drives and Micro-Motor Systems; 11th GMM/ETG-Symposium (S. 148–153). VDE Verlag GmbH. Abgerufen von https://ieeexplore.ieee.org/document/8241192 (Original work published 2025)
Hagemann, T., Haizmann, F., Schlipf, D., & Cheng, P. W. (2017). Realistic simulations of extreme load cases with lidar-based feedforward control. In German Wind Energy Conference. Bremen, Germany. http://doi.org/10.18419/opus-9333 (Original work published 2025)
Yu, W., Lemmer, F., Bredmose, H., Borg, M., Jurado, P., Mikkelsen, R. F., … Cheng, P. W. (2017). The triple spar campaign: implementation and test of a blade pitch controller on a scaled floating wind turbine model. In Energy Procedia (Bd. 137, S. 323–338). Trondheim, Norway. http://doi.org/10.1016/j.egypro.2017.10.357 (Original work published 2025)
Borraccino, A., Schlipf, D., Haizmann, F., & Wagner, R. (2017). Wind field reconstruction from nacelle-mounted lidar short-range measurements. Wind Energy Science, 2, 269–283. http://doi.org/10.5194/wes-2-269-2017 (Original work published 2025)