Adaptive agents in the House of Quality
AbstractManaging 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 2835.
Date of creation: Jul 1999
Date of revision:
new product development; total quality management; quality function deployment; information flow; organisational learning; learning classifier systems; genetic algorithms;
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