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Titel: I'll tell you what I want: Categorization of Pareto Fronts for Automated Rule-based Decision-Making
VerfasserIn: Hoffmann, Matthias K.
Schmitt, Thomas
Flaßkamp, Kathrin
HerausgeberIn: Filippova, Tatiana
Sprache: Englisch
Titel: IFAC-PapersOnLine
Bandnummer: 55
Heft: 16
Seiten: 376-381
Verlag/Plattform: Elsevier
Erscheinungsjahr: 2022
Erscheinungsort: Amsterdam
Konferenzort: Gif sur Yvette, France
Freie Schlagwörter: Multi-objective Optimization
Model predictive Control
Decision-making
Energy Management Systems
Optimal Control
DDC-Sachgruppe: 620 Ingenieurwissenschaften und Maschinenbau
Dokumenttyp: Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag)
Abstract: The application of Pareto optimization in control engineering requires decision-making as a downstream step since one solution has to be selected from the set of computed Pareto optimal points. Economic Model Predictive Control (MPC) requires repeated optimization and, in multi-objective optimization problems, selection of Pareto optimal points at every time step. Thus, designing an automated selection strategy is favorable. However, it is challenging to come up with a measure – possibly based on a Pareto front analysis – that characterizes preferred Pareto optimal points uniformly across different Pareto fronts. In this work, we first discuss these difficulties for application within MPC and then suggest a solution based on unsupervised machine learning methods. The approach is based on categorizing Pareto fronts as an intermediate step. This allows generating an individual set of rules for every category. Thereby, the human decision-maker's preferences can be modeled more accurately and the selection of a Pareto optimal solution becomes less time-consuming while breaking down the decision-making process into a selection solely based on the Pareto front's shape. Here, the measures act as anchor points for the decision rules. Lastly, a novel knee point measure, i.e. an approximation of the Pareto front's curvature, is presented and used for a knee point-focused categorization. The proposed algorithm is successfully applied to a case study for an energy management system. Moreover, we compare our method to using singular measures for decision-making in order to show its higher flexibility leading to better performance of the controller.
DOI der Erstveröffentlichung: 10.1016/j.ifacol.2022.09.053
URL der Erstveröffentlichung: https://www.sciencedirect.com/science/article/pii/S2405896322012290
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-433391
hdl:20.500.11880/38882
http://dx.doi.org/10.22028/D291-43339
ISSN: 2405-8963
Datum des Eintrags: 5-Nov-2024
Bemerkung/Hinweis: IFAC-PapersOnLine, Volume 55, Issue 16, 2022, Pages 376-381
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Univ.-Prof. Dr. Kathrin Flaßkamp
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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