@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}
}
@inproceedings{LeiseAltherr2018,
  author    = {Leise, Philipp and Altherr, Lena},
  title     = {Optimizing the design and control of decentralized water supply systems - a case-study of a hotel building},
  series = {EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization},
  booktitle = {EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-97773-7},
  doi       = {10.1007/978-3-319-97773-7_107},
  pages     = {1241 -- 1252},
  year      = {2018},
  abstract  = {To increase pressure to supply all floors of high buildings with water, booster stations, normally consisting of several parallel pumps in the basement, are used. In this work, we demonstrate the potential of a decentralized pump topology regarding energy savings in water supply systems of skyscrapers. We present an approach, based on Mixed-Integer Nonlinear Programming, that allows to choose an optimal network topology and optimal pumps from a predefined construction kit comprising different pump types. Using domain-specific scaling laws and Latin Hypercube Sampling, we generate different input sets of pump types and compare their impact on the efficiency and cost of the total system design. As a realistic application example, we consider a hotel building with 325 rooms, 12 floors and up to four pressure zones.},
  language  = {en}
}
@incollection{LeiseAltherrPelz2018,
  author    = {Leise, Philipp and Altherr, Lena and Pelz, Peter F.},
  title     = {Energy-Efficient design of a water supply system for skyscrapers by mixed-integer nonlinear programming},
  series = {Operations Research Proceedings 2017},
  booktitle = {Operations Research Proceedings 2017},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-89919-0},
  doi       = {10.1007/978-3-319-89920-6_63},
  year      = {2018},
  abstract  = {The energy-efficiency of technical systems can be improved by a systematic design approach. Technical Operations Research (TOR) employs methods known from Operations Research to find a global optimal layout and operation strategy of technical systems. We show the practical usage of this approach by the systematic design of a decentralized water supply system for skyscrapers. All possible network options and operation strategies are modeled by a Mixed-Integer Nonlinear Program. We present the optimal system found by our approach and highlight the energy savings compared to a conventional system design.},
  language  = {en}
}