IJPAM: Volume 61, No. 3 (2010)


Jorge Domínguez-Domínguez$^1$7777 Katy Fwy, #4103, Howston, TX 77024, USA, J.A. Domínguez-López$^2$
$^1$Probability and Statistics Research Group
Mathematics Research Center
P.O. Box 420, Guanajuato, 36000, MEXICO
e-mail: [email protected]
$^2$Conteck, P.O. Box 27, Guanajuato, 36082, MEXICO
e-mail: [email protected]

Abstract.Improvement is a goal that is present in several of our daily activities and projects. Currently, improvement is a necessity for industrial processes. Optimization methods play a vital role to achieve this goal, as these methods allow evaluating the existence and significance of the improvement. In addition, these methodologies are relevant in the planning of the strategy which has to be followed in order to improve the process characteristics. Thus, in this paper, four optimization methods that utilize fuzzy set theory and multiple attribute decision making are proposed. These have been shown to be more efficient when comparing them through an example with other classic methods of statistical optimization. An industrial example has been used as a baseline for this comparison. The results are illustrated using a graphic technique of optimization.

Received: March 15, 2010

AMS Subject Classification: 90-08

Key Words and Phrases: continuous improvement, optimization, regression models, experimental design, fuzzy optimization, level curves

Source: International Journal of Pure and Applied Mathematics
ISSN: 1311-8080
Year: 2010
Volume: 61
Issue: 3