A FUZZY-GUIDED GENETIC ALGORITHM FOR QUALITY ENHANCEMENT IN THE SUPPLY CHAIN

Cassandra X. H. Tang, Henry C. W. Lau

2009

Abstract

To respond to the globalization and fierce competition, manufacturers gradually realize the challenge of demanding customers who strongly seek for products of high-quality and low-cost, which implicitly calls for the quality improvement of the products in a cost-effective way. Traditional methods focused on specified process optimization for quality enhancement instead of emphasizing the organizational collaboration to ensure qualitative performance. This paper introduces artificial intelligence (AI) approach to attain quality enhancement by automating the selection of process parameters within the supply chain. The originality of this research is providing an optimal configuration of process parameters along the supply chain and delivering qualified outputs to raise customer satisfaction.

References

  1. Bayraktar, D. (1998), A knowledge-based expert system approach for the auditing process of some elements in the quality assurance system, Int. J. Production Economics, vol. 5657, pp. 37-46.
  2. Bernardy, G. and Scherff, B. (1998), SPOC - process modelling provides on-line quality control and predictive process control in particle and fibreboard production, Proceedings of the 24th Annual Conference of IEEE Industrial Electronics Society, IECON'98, 31.08.-04.09., Aachen.
  3. Heinloth, S. (2001), Measuring quality's return on investment, Quality Yearbook 2001, McGraw-Hill, New York, NY.
  4. Yang, J.B., Liu, J. , Xu, D.L., Wang, J. and Wang, H.W. (2007), Optimization Models for Training BeliefRule-Based Systems, IEEE Trans. Syst., Man Cybern. A, Syst., Humans, vol. 37, no. 4, pp. 569-585.
  5. Kobbacy, K., Vadera, S. and Rasmy, M.H. (2007), AI and OR in management of operations: history and trends, Journal of the Operational Research Society, vol.58, pp. 10-28.
  6. Tana, K.H., Limb, C.P., Plattsc, K. and Koay, H.S. (2006), An intelligent decision support system for manufacturing technology investments, Int. J. Production Economics, vol. 104, pp. 179-90.
  7. Yu, F., Tu, F. and Pattipati, K.R. (2006), A novel congruent organizational design methodology using group technology and a nested genetic algorithm, IEEE Trans. Syst., Man Cybern. A, Syst., Humans, vol. 36, no. 1, pp. 5-18.
  8. Chiang, T.C., Huang, A.C. and Fu, L.C. (2007), Modeling, scheduling, and performance evaluation for wafer fabrication: a queueing colored Petri-net and GAbased approach, IEEE Transactions on Automation Science and Engineering, vol. 3, no. 3, pp. 912-918.
  9. Wang, C.H., Hong, T.P. and Tseng, S.S. (1998), Integrating fuzzy knowledge by genetic algorithms, IEEE Transactions on Evolutionary Computation, vol.2, no. 4, pp. 138-149.
  10. Lau, H.C.W., Ho, G.T.S., Chu, K.F., Ho, W. and Lee, C.K.M. (2009), Development of an intelligent quality management system using fuzzy association rules, Expert Systems with Application, vol.36, no. 2, pp. 1801-1815.
  11. Hwang, S.F. and He, R.S. (2006), Improving realparameter genetic algorithm with simulated annealing for engineering problems, Advances in Engineering Software, vol. 37, pp. 406-18.
  12. Ho, G.T.S., Lau, H.C.W., Chung S.H., Fung R.Y.K., Chan, T.M. and Lee, C.K.M (2008), Development of an intelligent quality management system using fuzzy association rules, Industrial Management & Data Systems, vol.108, no. 7, pp. 947-972.
  13. Winnall, S., and Winderbaum, S, (2000), Lithium Niobate Reactive Ion Etching Electronic Warfare Division, DSTO Electronics and Surveillance Research Laboratory, DSTO-TN-0291.
Download


Paper Citation


in Harvard Style

Tang C. and Lau H. (2009). A FUZZY-GUIDED GENETIC ALGORITHM FOR QUALITY ENHANCEMENT IN THE SUPPLY CHAIN . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 86-90. DOI: 10.5220/0001865100860090


in Bibtex Style

@conference{iceis09,
author={Cassandra X. H. Tang and Henry C. W. Lau},
title={A FUZZY-GUIDED GENETIC ALGORITHM FOR QUALITY ENHANCEMENT IN THE SUPPLY CHAIN},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={86-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001865100860090},
isbn={978-989-8111-85-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A FUZZY-GUIDED GENETIC ALGORITHM FOR QUALITY ENHANCEMENT IN THE SUPPLY CHAIN
SN - 978-989-8111-85-2
AU - Tang C.
AU - Lau H.
PY - 2009
SP - 86
EP - 90
DO - 10.5220/0001865100860090