TY  - CHAP
A1  - Altherr, Lena
A1  - Ederer, Thorsten
A1  - Lorenz, Ulf
A1  - Pelz, Peter F.
A1  - Pöttgen, Philipp
ED  - Lübbecke, Marco E.
ED  - Koster, Arie
ED  - Letmathe, Peter
ED  - Madlener, Reihard
ED  - Preis, Britta
ED  - Walther, Grit
T1  - Designing a feedback control system via mixed-integer programming
T2  - Operations Research Proceedings 2014: Selected Papers of the Annual International Conference of the German Operations Research
N2  - 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.
KW  - Optimal Topology
KW  - Controller Parameter
KW  - Level Control System
KW  - Technical Operation Research
KW  - Optimal Closed Loop
Y1  - 2016
SN  - 978-3-319-28695-2
U6  - http://dx.doi.org/10.1007/978-3-319-28697-6_18
SP  - 121
EP  - 127
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Altherr, Lena
A1  - Ederer, Thorsten
A1  - Schänzle, Christian
A1  - Lorenz, Ulf
A1  - Pelz, Peter F.
T1  - Algorithmic system design using scaling and affinity laws
T2  - Operations Research Proceedings 2015
N2  - 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.
KW  - Optimal Topology
KW  - Piecewise Linearization
KW  - Ventilation System
KW  - Similarity Theory
Y1  - 2017
SN  - 978-3-319-42901-4
SN  - 978-3-319-42902-1
U6  - http://dx.doi.org/10.1007/978-3-319-42902-1
N1  - International Conference of the German, Austrian and Swiss Operations Research Societies (GOR, ÖGOR, SVOR/ASRO), University of Vienna, Austria, September 1-4, 2015
SP  - 605
EP  - 611
PB  - Springer
CY  - Cham
ER  -