Derivation of Decision Rules for the Evaluation of Product Performance Using Genetic Algorithms and Rough Set Theory

  • Zhai Lian-Yin
  • Khoo Li-Pheng
  • Fok Sai-Cheong
Part of the Massive Computing book series (MACO, volume 3)


In the manufacturing of critical components of a product, it is important to ascertain the performance and behaviour of those components being produced before assembly. Frequently, these part components are subject to stringent acceptance tests in order to confirm their conformance to the required specifications. Such acceptance tests are normally monotonous and tedious. At times, they may be costly to carry out and may affect the cycle time of production. This work proposes an approach that is based on genetic algorithms and rough set theory to uncover the characteristics of the part components in relation to their performance using past acceptance test data, that is, the historical data. Such characteristics are described in terms of decision rules. By examining the characteristics exhibited, it may be possible to relax the rigour of acceptance tests. A case study was used to illustrate the proposed approach. It was found that the cost in conducting the acceptance tests and the production cycle time could be reduced remarkably without compromising the overall specifications of the acceptance tests.


Decision Rule Acceptance Test Information Table Rule Pruner Redundant Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Fausett, L.V., Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1994.zbMATHGoogle Scholar
  2. Goldberg, D.E., Genetic Algorithms in Search, Optimisation and Machine Learning. Reading, Mass.: Addison-Wesley Pub. Co., 1989.zbMATHGoogle Scholar
  3. Khoo, L.P., Tor, S.B. and Zhai, L.Y., “A Rough-set Based Approach for Classification and Rule Induction,” International Journal of Advanced Manufacturing Technology, 15, 438–444, 1999.CrossRefGoogle Scholar
  4. Khoo, L.P. and Zhai, L.Y., “R. Class: A Prototype Rough-set and Genetic Algorithms Enhanced Multi-concept Classification System for Manufacturing Diagnosis,” in Handbook of Computational Intelligence in Design and Manufacturing, Boca Raton: CRC Press LLC, 2000 (in press).Google Scholar
  5. Mitchell, J.S., An Introduction to Machinery Analysis and Monitoring, Tulsa, Oklahoma: PannWell Books Company, 1981.Google Scholar
  6. Pawlak, Z., “Rough Set Approach to Multi-attribute Decision Analysis,” European Journal of Operational Research, 72 (3), 443–459, 1994.MathSciNetCrossRefzbMATHGoogle Scholar
  7. Pawlak, Z., “Rough Set: A New Approach to Vagueness,” in Fuzzy Logic for the Management of Uncertainty, pp. 105–108, New York: John Wiley and Sons, 1992.Google Scholar
  8. Pawlak, Z., “Rough Sets,” in Rough Sets and Data Mining - Analysis for Imprecise Data, pp. 38, Boston, Mass: Kluwer Academic Publishers, 1997.Google Scholar
  9. Pawlak, Z., “Rough Sets,” International Journal of Computer and Information Sciences, 11 (5), 341–356, 1982.MathSciNetCrossRefzbMATHGoogle Scholar
  10. Pawlak, Z, Z., “Why Rough Sets,” in 1996 IEEE International Conference on Fuzzy Systems, pp. 738–743, 1996.Google Scholar
  11. Quinlan, J.R., “Induction of Decision Trees,” Machine Learning, 1, 81–106, 1986.Google Scholar
  12. Quinlan, J.R., C4.5: Programs for Machine Learning, Boston: Morgan Kaufmann Publishers, 1992.Google Scholar
  13. Shafer, G., “Belief Functions and Parametric Models,” Journal of Royal Statistical Socirety, 44, 322–352, 1982.MathSciNetzbMATHGoogle Scholar
  14. Shafer, G., A Mathematical Theory of Evidence, Princeton, NN.JY.: Princeton Univ. Press, 1976.Google Scholar
  15. Wong, S.K.M., Ziarko, W. and Li, Y.R., “Comparison of Rough-set and Statistical Methods in Inductive Learning,” International Journal of Man-Machine Studies, 24, 53–72, 1986.CrossRefzbMATHGoogle Scholar
  16. Zadeh, L.A., “Fuzzy Sets,” Information and Control, 8, 338–353, 1965.MathSciNetCrossRefzbMATHGoogle Scholar
  17. Zhai, L.Y., Automated Extraction of Diagnostic Knowledge, Master thesis, Nanyang Technological University, Singapore, 2000.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Zhai Lian-Yin
    • 1
  • Khoo Li-Pheng
    • 1
  • Fok Sai-Cheong
    • 1
  1. 1.School of Mechanical and Production EngineeringNanyang Technological UniversitySingapore

Personalised recommendations