@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}
}
@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}
}