Adaptive agents in the House of Quality
Managing the information flow within a big organization is a challenging task. Moreover, in a distributed decision-making process conflicting objectives occur. In this paper, artificial adaptive agents are used to analyze this problem. The decision makers are implemented as Classifier Systems, and their learning process is simulated by Genetic Algorithms. To validate the outcomes we compared the results with the optimal solutions obtained by full enumeration. It turned out that the genetic algorithm indeed was able to generate useful rules that describe how the decision makers involved in new product development should react to the requests they are required to fulfill.
|Date of creation:||Jul 1999|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:2835. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht)
If references are entirely missing, you can add them using this form.