@incollection{StengerAltherrAbel2019,
  author    = {Stenger, David and Altherr, Lena and Abel, Dirk},
  title     = {Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead},
  series = {Operations Research Proceedings 2018},
  booktitle = {Operations Research Proceedings 2018},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-18499-5 (Print)},
  doi       = {10.1007/978-3-030-18500-8_47},
  pages     = {379 -- 385},
  year      = {2019},
  abstract  = {In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead.},
  language  = {en}
}
@inproceedings{MuellerAltherrLeiseetal.2020,
  author    = {M{\"u}ller, Tim M. and Altherr, Lena and Leise, Philipp and Pelz, Peter F.},
  title     = {Optimization of pumping systems for buildings: Experimental validation of different degrees of model detail on a modular test rig},
  series = {Operations Research Proceedings 2019},
  booktitle = {Operations Research Proceedings 2019},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-48438-5},
  doi       = {10.1007/978-3-030-48439-2_58},
  pages     = {481 -- 488},
  year      = {2020},
  abstract  = {Successful optimization requires an appropriate model of the system under consideration. When selecting a suitable level of detail, one has to consider solution quality as well as the computational and implementation effort. In this paper, we present a MINLP for a pumping system for the drinking water supply of high-rise buildings. We investigate the influence of the granularity of the underlying physical models on the solution quality. Therefore, we model the system with a varying level of detail regarding the friction losses, and conduct an experimental validation of our model on a modular test rig. Furthermore, we investigate the computational effort and show that it can be reduced by the integration of domain-specific knowledge.},
  language  = {en}
}