@inproceedings{AltherrEdererSchaenzleetal.2017,
  author    = {Altherr, Lena and Ederer, Thorsten and Sch{\"a}nzle, Christian and Lorenz, Ulf and Pelz, Peter F.},
  title     = {Algorithmic system design using scaling and affinity laws},
  series = {Operations Research Proceedings 2015},
  booktitle = {Operations Research Proceedings 2015},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-42901-4},
  doi       = {10.1007/978-3-319-42902-1},
  pages     = {605 -- 611},
  year      = {2017},
  abstract  = {Energy-efficient components do not automatically lead to energy-efficient systems. Technical Operations Research (TOR) shifts the focus from the single component to the system as a whole and finds its optimal topology and operating strategy simultaneously. In previous works, we provided a preselected construction kit of suitable components for the algorithm. This approach may give rise to a combinatorial explosion if the preselection cannot be cut down to a reasonable number by human intuition. To reduce the number of discrete decisions, we integrate laws derived from similarity theory into the optimization model. Since the physical characteristics of a production series are similar, it can be described by affinity and scaling laws. Making use of these laws, our construction kit can be modeled more efficiently: Instead of a preselection of components, it now encompasses whole model ranges. This allows us to significantly increase the number of possible set-ups in our model. In this paper, we present how to embed this new formulation into a mixed-integer program and assess the run time via benchmarks. We present our approach on the example of a ventilation system design problem.},
  language  = {en}
}