Algorithmic System Design Using Scaling and Affinity Laws

Lena C. Altherr (), Thorsten Ederer (), Christian Schänzle (), Ulf Lorenz () and Peter F. Pelz ()
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Lena C. Altherr: TU Darmstadt
Thorsten Ederer: TU Darmstadt
Christian Schänzle: TU Darmstadt
Ulf Lorenz: Universität Siegen
Peter F. Pelz: TU Darmstadt

A chapter in Operations Research Proceedings 2015, 2017, pp 605-611 from Springer

Abstract: 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.

Keywords: Optimal Topology; Piecewise Linearization; Ventilation System; Similarity Theory; Model Range (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-42902-1_82

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