Arbeitspapier
Uncertainty of the optimum influence factor levels in multicriteria optimization using the concept of desirability
The Desirability Index (DI) is a widely used method for multicriteria optimization in industrial quality control, by which optimal levels of the process influencing factors are determined in order to archieve maximum process quality. In practice however situations may occur in which slight changes of these factor levels lead to lower production costs or to facilitation of the production process and therefore would be preferred. In this paper an innovative approach for measuring the effect of these changes on the DI based on its distribution is introduced.
- Sprache
-
Englisch
- Erschienen in
-
Series: Technical Report ; No. 2004,23
- Thema
-
Multikriteria-Verfahren
Qualitätsmanagement
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Trautmann, Heike
Weihs, Claus
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (wo)
-
Dortmund
- (wann)
-
2004
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Trautmann, Heike
- Weihs, Claus
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Entstanden
- 2004
Ähnliche Objekte (12)
Desirability to characterize process capability
Pareto-Optimality and Desirability Indices
Ensuring an efficient turning process by means of desirability index optimization for correlated quality criteria
Optimal vs. classical linear dimension reduction
D-optimal plans for variable selection in data bases
Clustering techniques for the detection of business cycles
Incorporating background knowledge for better prediction of cycle phases
Direct minimization of error rates in multivariate classification
Stability of multivariate representation of business cycles over time
Comparing classifiers in standardized partition spaces using experimental design
Univariate characterization of the German business cycle 1955-1994
Dynamic Bayesian networks for classification of business cycles
Desirability to characterize process capability
Pareto-Optimality and Desirability Indices
Ensuring an efficient turning process by means of desirability index optimization for correlated quality criteria
Optimal vs. classical linear dimension reduction
D-optimal plans for variable selection in data bases
Clustering techniques for the detection of business cycles
Incorporating background knowledge for better prediction of cycle phases
Direct minimization of error rates in multivariate classification
Stability of multivariate representation of business cycles over time
Comparing classifiers in standardized partition spaces using experimental design
Univariate characterization of the German business cycle 1955-1994
Dynamic Bayesian networks for classification of business cycles
Desirability to characterize process capability
Pareto-Optimality and Desirability Indices
Ensuring an efficient turning process by means of desirability index optimization for correlated quality criteria
Optimal vs. classical linear dimension reduction
D-optimal plans for variable selection in data bases
Clustering techniques for the detection of business cycles
Incorporating background knowledge for better prediction of cycle phases
Direct minimization of error rates in multivariate classification
Stability of multivariate representation of business cycles over time
Comparing classifiers in standardized partition spaces using experimental design
Univariate characterization of the German business cycle 1955-1994