@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}
}
@incollection{AltherrEdererLorenzetal.2016,
  author    = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp},
  title     = {Designing a feedback control system via mixed-integer programming},
  series = {Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research},
  booktitle = {Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research},
  editor    = {L{\"u}bbecke, Marco E. and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Preis, Britta and Walther, Grit},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-28695-2},
  doi       = {10.1007/978-3-319-28697-6_18},
  pages     = {121 -- 127},
  year      = {2016},
  abstract  = {Pure analytical or experimental methods can only find a control strategy for technical systems with a fixed setup. In former contributions we presented an approach that simultaneously finds the optimal topology and the optimal open-loop control of a system via Mixed Integer Linear Programming (MILP). In order to extend this approach by a closed-loop control we present a Mixed Integer Program for a time discretized tank level control. This model is the basis for an extension by combinatorial decisions and thus for the variation of the network topology. Furthermore, one is able to appraise feasible solutions using the global optimality gap.},
  language  = {en}
}