Adaptive agents in the House of Quality

Abstract

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.new product development; total quality management; quality function deployment; information flow; organisational learning; learning classifier systems; genetic algorithms