Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A case-based expert support system for due-date assignment in a wafer fabrication factory

  • 112 Accesses

  • 39 Citations

Abstract

Owing to the complexity of wafer fabrication, the traditional human approach to assigning due-date is imprecise and very prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due date to each order becomes a challenge to the production planning and scheduling staff. Since most production orders are similar to those previously manufactured, the case based reasoning (CBR) approach provides a suitable means for solving the due-date assignment problem. This research proposes a CBR approach that employs the k-nearest neighbors concept with dynamic feature weights and non-linear similarity functions. The test results show that the proposed approach can more accurately predict order due dates than other approaches.

This is a preview of subscription content, log in to check access.

References

  1. Brill, F. Z., Brown, D. E. and Martin, W. N. (1992) Fast genetic selection of features for neural network classifiers. neural networks, IEEE Transactions on 32, 324-328.

  2. Cheng, T. C. and Gupta, E. (1985) Survey of scheduling research involving due-date determination decisions. European Journal of Operational Research, 36(11), 1017-1026.

  3. Conway, R. W., Maxwell, W. L. and Miller, L. W. (1967) Theory of Scheduling, Addison-Wesley, Massachusetts.

  4. Dhar, V. and Strin, R. (1997) Intelligent Decision Support Methods, Prentice Hall, International, Inc., p. 156.

  5. Finnie, G. R. and Witting, G. E. (1995) Estimating software development effort with case-based reasoning. Proc. 2nd International Conference on Case-based Reasoning, Springer Verlag.

  6. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Mich., USA.

  7. Jo, H., Han, I. and Lee, H. (1997) Bankruptcy prediction using case-based reasoning, neural networks and discriminant analysis. Expert Systems and Applications, 13, 97-108.

  8. Kelly, J. D. and Davis, L. (1991) A hybrid genetic algorithm for classification. International Joint Conference on Artificial Intelligence, 645-650.

  9. Kim, S. H. and Shin, S. W. (2000) Identifying the impact of decision variables for nonlinear classification tasks. Expert Systems with Applications, 18, 201-214.

  10. Kohavi, R., Langley, P. and Yun, Y., The Utility of Feature Weighting in Nearest-Neighbor Algorithms. ECML-97 (poster).

  11. Kolodner, J. L. (1992) An introduction to case-based reasoning. Artificial Intelligence Review, 6, 3-34.

  12. Langley, P. and Iba, W. (1993) Average case analysis of a nearest neighbor algorithm, in Proceedings of the Thirteenth IJCAI, Morgan Kaufmann, Chambery, France, pp. 889-894.

  13. Liao, T. W., Zhang, Z. M. and Mount, C. R. (2000) A case-based reasoning system for identifying failure mechanisms. Engineering Applications of Artificial Intelligence, 13, 199-213.

  14. Medin, D. L. and Schaffer, M. M. (1978) Context theory of classification learning. Psychological Review, 85, 207-238.

  15. Shin, K. and Han, I. (1999) Case-based reasoning supported by genetic algorithms for corporate bond rating. Expert Systems with Applications, 16, 85-95.

  16. Siedlecki, W. and Sklansky, J. (1989) A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10, 335-347.

  17. Watson, I. and Gardingen, D. (1999) A distributed cased-based reasoning application for engineering sales support, in Proc. 16th Int. Joint Conf. on Artificial Intelligence (IJCAI-99), 1, 600-605.

  18. Watson, I. and Watson, H. (1997) CAIRN: A case-based document retrieval system, in Proc. of the 3rd United Kingdom Case-Based Reasoning Wrokshop, University of Manchester, Filer, N. & Watson, I. (eds).

  19. Wettschereck, D., Aha, D. W. and Mohri, T. (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning Algorithms. Artificial Intelligence Review, 11, 273-314.

  20. Wilson, D. R. and Martinez, T. R. (1996) Instance based learning with genetically derived attribute weights, in Proceedings of the International Conference on Artificial Intelligence, Expert Systems, and Neural Networks, pp. 11-14.

Download references

Author information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Chiu, C., Chang, P. & Chiu, N. A case-based expert support system for due-date assignment in a wafer fabrication factory. Journal of Intelligent Manufacturing 14, 287–296 (2003). https://doi.org/10.1023/A:1024693524603

Download citation

  • Due-date assignment
  • genetic algorithms
  • case-based reasoning