Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling

International Journal of Production Research

Volumn 44, Issue 21, 2006, Pages 4491-4509

  Li, D C ^a   Wu, C ^a   Chang, F M ^b  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b TUNG FANG INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Flexible manufacturing system; Machine learning; Scheduling; Small data set

Indexed keywords

DATA REDUCTION; DECISION MAKING; FUZZY SETS; LEARNING SYSTEMS; MEMBERSHIP FUNCTIONS; SCHEDULING;

FUZZIFICATION; LEARNING ACCURACY; LEARNING PROBLEMS; SMALL DATA SET;

FLEXIBLE MANUFACTURING SYSTEMS;

EID: 33748694681     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207540600559849     Document Type: Article

References (17)

1. Anthony, M. and Biggs, N., Computational learning theory, 1997 (Cambridge University Press: Cambridge).
2. Chen, C.C. and Yih, Y., Identifying attributes for knowledge-based development in dynamic scheduling environments. Int. J. Prod. Res., 1996, 34, 1739-1755.
3. Huang, C. and Moraga, C., A diffusion-neural-network for learning from small samples. Int. J. Approx. Reason., 2004, 35, 137-161.
4. Huang, C.F., Principle of information diffusion. Fuzzy Sets Sys., 1997, 91, 69-90.
5. Jang, J.S.R., ANFIS; adaptive-network-based fuzzy inference systems. IEEE Trans. Sys. Man Cybernet., 1993, 23, 665-685.
6. Li, D.C., Chen, L.S. and Lin, Y.S., Using functional virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environments. Int. J. Prod. Res., 2003, 41, 4011-4024.
7. Li, D.C., Wu, C. and Chang, F.M., Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy. Int. J. Adv. Manuf. Tech., 2005, 27(3-4), 321-328.
8. Li, D.C., Wu, C., Tsia, T.I. and Chang, F.M., Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge. Comput. Oper. Res., 2006, 33(6), 1857-1869.
9. Nakasuka, S. and Yoshida, T., Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool. Int. J. Prod. Res., 1992, 30, 411-431.
10. Niyogi, P., Girosi, F. and Tomaso, P., Incorporating prior information in machine learning by creating virtual examples. Proc. IEEE, 1998, 86(11), 2196-2209.
11. Pierreval, H. and Ralambondrainy, H., A simulation and learning technique for generating knowledge about manufacturing systems behavior. J. Oper. Res. Soc., 1990, 41, 461-474.
12. Priore, P., De la Fuente, D., Gomez, A. and Puente, J., A review of machine learning in dynamic scheduling of flexible manufacturing systems. AIEDAM, 2001, 15, 251-263.
13. Quinlan, J.R., Learning decision tree classifiers. ACM Comput. Surv., 1996, 28, 71-72.
14. Sabuncuoglu, I. and Touhami, S., Simulation metamodelling with neural networks: an experimental investigation. Int. J. Prod. Res., 2002, 40, 2483-2505.
15. Shaw, M.J., Park, S. and Raman, N., Intelligent scheduling with machine learning capabilities: the induction of scheduling knowledge. IIE Trans., 1992, 24, 156-168.
16. Srinivasan, M. and Moon, Y.B., A framework for a goal-driven approach to group technology applications using conceptual clustering. Int. J. Prod. Res., 1997, 35, 847-866.
17. Sun, Y.L. and Yih, Y., An intelligent controller for manufacturing cells. Inr. J. Prod. Res., 1996, 34, 2353-2373.