Fent, Thomas (1999): Adaptive agents in the House of Quality.
Download (211kB) | Preview
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.
|Item Type:||MPRA Paper|
|Institution:||Vienna University of Economics and Business Administration|
|Original Title:||Adaptive agents in the House of Quality|
|Keywords:||new product development; total quality management; quality function deployment; information flow; organisational learning; learning classifier systems; genetic algorithms|
|Subjects:||M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M3 - Marketing and Advertising > M31 - Marketing
M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M1 - Business Administration > M11 - Production Management
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis
|Depositing User:||Thomas Fent|
|Date Deposited:||20. Apr 2007|
|Last Modified:||21. Feb 2013 18:45|
Chase, R. B. and Aquilano, N. J. (1995). Production and Operations Management, Manufacturing and Services, chapter 5, pages 173-180. Richard D. Irwin, Inc. 7 edition
Dawid, H. (1996). Adaptive Learning by Genetic Algorithms, Analytical Results and Applications to Economic Models, volume 441 of Lecture Notes in Economics and Mathematical Systems. Springer.
Geyer-Schulz, A. (1995). Holland classifer sytems. APL Quote Quad, 25(4):43-55. ACM SIGAPL
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company.
Hauser, J. R. and Clausing, D. (1988) The house of quality. Harvard Business Review, pages 63-73.
Holland, J. H. (1976). Adaption. In Progress in Theoretical Biology IV. ed. Rosen, R. F., New York, Academic Press.
Holland, J. H. (1995). Adaption in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press/Bradford Books edition, Ann Arbor, MI.