Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Computers and Operations Research

Volumn 33, Issue 6, 2006, Pages 1857-1869

  Li, Der Chiang ^a   Wu, Chih Sen ^a   Tsai, Tung I ^b   Chang, Fengming M ^c  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b DIWAN COLLEGE OF MANAGEMENT   (Taiwan)

^c TUNG FANG INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

ANFIS; Data trend; Flexible manufacturing system; Mega fuzzification; Scheduling; Small data set

Indexed keywords

COMPUTER SIMULATION; DATA REDUCTION; FUZZY CONTROL; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; PROBLEM SOLVING;

ANFIS; DATA TREND; FLEXIBLE MANUFACTURING SYSTEM; MEGA-FUZZIFICATION; SMALL DATA SET;

SCHEDULING;

EID: 27344432371     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2004.11.022     Document Type: Article

References (19)

1. D.C. Li, L.S. Chen, and Y.S. Lin Using functional virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environments International Journal of Production Research 41 2003 4011 4024
2. D.C. Li, C.S. Wu, and K.Y. Tong Using an unsupervised neural network and decision tree as knowledge acquisition tools for FMS scheduling International Journal of Systems Science 28 1997 977 985
3. D.C. Li, K.L. Han, and K.Y. Tong A strategy for evolution of algorithms to increase the computational effectiveness of NP-hard scheduling problems European Journal of Operational Research 88 1996 404 412
4. D.C. Li, and I.S. She Using unsupervised learning technologies and simulation analysis to induce scheduling knowledge for flexible manufacturing systems International Journal of Production Research 32 1994 2187 2199
5. P. Priore, D. De La Fuente, A. Gomez, and J. Puente A review of machine learning in dynamic scheduling of flexible manufacturing systems Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15 2001 251 263
6. M.J. Shaw, S. Park, and N. Raman Intelligent scheduling with machine learning capabilities the induction of scheduling knowledge IIE Transactions 24 1992 156 168
7. J.R. Quinlan Learning decision tree classifiers ACM Computing Surveys 28 1996 71 72
8. S. Nakasuka, and T. Yoshida Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool International Journal of Production Research 30 1992 411 431
9. H. Pierreval, and H. Ralambondrainy A simulation and learning technique for generating knowledge about manufacturing systems behavior Journal of the Operational Research Society 41 1990 461 474
10. Y.L. Sun, and Y. Yih An intelligent controller for manufacturing cells International Journal of Production Research 34 1996 2353 2373
11. I. Sabuncuoglu, and S. Touhami Simulation metamodelling with neural networks an experimental investigation International Journal of Production Research 40 2002 2483 2505
12. C.C. Chen, and Y. Yih Identifying attributes for knowledge-based development in dynamic scheduling environments International Journal of Production Research 34 1996 1739 1755
13. C. Huang, and C. Moraga A diffusion-neural-network for learning from small samples International Journal of Approximate Reasoning 35 2004 137 161
14. Li DC, Wu CS, Chang FM. Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy. International Journal of Advanced Manufacturing Technology, 2003, in press.
15. J.S.R. Jang ANFIS Adaptive-network-based fuzzy inference systems IEEE Transactions on Systems, Man and Cybernetics 23 1993 665 685
16. M. Sugeno, and G.T. Kang Structure identification of fuzzy model Fuzzy Sets and Systems 28 1988 15 23
17. Takagi T, Sugeno M. Derivation of fuzzy control rules from human operator's control actions. In: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, 1983; 55-60.
18. T. Takagi, and M. Sugeno Fuzzy identification of systems and its applications to modeling and control IEEE Transactions on Systems, Man and Cybernetics 15 1985 116 132
19. M. Srinivasan, and Y.B. Moon A framework for a gold-driven approach to group technology applications using conceptual clustering International Journal of Production Research 35 1997 1759 1773