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Optimierung

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Statistische Versuchsplanung

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Zusammenfassung

Die Optimierung komplexer Systeme läuft zumeist auf einen Kompromiss zwischen verschiedenen Qualitätsgrößen hinaus. Eine sinnvolle Zusammenfassung auf eine globale Qualitätsgröße ist dabei meist nicht sinnvoll, da der Lösungsbereich deutlich eingeschränkt und bereits vor der eigentlichen Optimierung eine noch unbekannte Lösung favorisiert wird. Dieses Kapitel befasst sich daher mit der multidimensionalen Optimierung, bei der alle Zielgrößen parallel betrachtet und optimiert werden. Aus den ermittelten Lösungen, die unterschiedliche Kompromisse zwischen den einzelnen Optimierungszielen enthalten, kann im Anschluss ein guter Kompromiss auf Basis aller vorhandenen Daten (Qualitätsgrößen plus zugehöriger Faktoreinstellungen) ausgewählt werden. Neben verschiedenen Grundlagen zur multidimensionalen Optimierung werden einige Verfahren aus dem Bereich genetischer (z.B. NSGAII) und naturanaloger Anlgorithmen (z.B. Particle Swarm) dargestellt.

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  1. Aldenhoven, Nordrhein-Westfalen, Deutschland

    Karl Siebertz

  2. Aachen, Deutschland

    David van Bebber

  3. Vaals, Niederlande

    Thomas Hochkirchen

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Siebertz, K., Bebber, D.v., Hochkirchen, T. (2017). Optimierung. In: Statistische Versuchsplanung. VDI-Buch. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55743-3_10

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  • DOI: https://doi.org/10.1007/978-3-662-55743-3_10

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