Development of two-level decision tree-based real-time scheduling system under product mix variety environment

Robotics and Computer-Integrated Manufacturing

Volumn 25, Issue 4-5, 2009, Pages 709-720

  Shiue, Yeou Ren ^a  

^a HUAFAN UNIVERSITY   (Taiwan)

Author keywords

Decision tree learning; Machine learning; Product mix variety; Real time scheduling; Self organizing map

Indexed keywords

DECISION TREE LEARNING; MACHINE LEARNING; PRODUCT MIX VARIETY; REAL-TIME SCHEDULING; SELF-ORGANIZING MAP;

CONFORMAL MAPPING; DECISION TREES; EDUCATION; FLEXIBLE MANUFACTURING SYSTEMS; KNOWLEDGE BASED SYSTEMS; ROBOT LEARNING; SCHEDULING;

REAL TIME SYSTEMS;

EID: 63449134271     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2008.06.002     Document Type: Article

References (37)

1. Martin MV. Design for variety: a methodology for developing product platform architectures. PhD thesis. Department of Mechanical Engineering, Stanford University, Stanford, 2000.
2. Cho H., and Wysk R.A. A robust adaptive scheduler for an intelligent workstation controller. Int J Prod Res 31 4 (1993) 771-789
3. Cho H., and Wysk R.A. Intelligent workstation controller for computer-integrated manufacturing: problems and models. J Manuf Syst 14 4 (1995) 252-263
4. Su C.T., and Shiue Y.R. Intelligent scheduling controller for shop floor control systems: a hybrid genetic algorithm/decision tree learning approach. Int J Prod Res 41 12 (2003) 2619-2641
5. Shiue Y.R., and Guh R.S. Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment. Robot Comput-Integrated Manuf 22 3 (2006) 203-216
6. Chung S.H., and Haung C.Y. The design of rapid production planning mechanism for the product mix changing in a wafer fabrication. J Chin Inst Ind Eng 20 2 (2003) 153-168
7. Arzi Y., and Iaroslavitz L. Operating an FMC by a decision-tree-based adaptive production control system. Int J Prod Res 38 3 (2000) 675-697
8. Priore P., De la Fuente D., Gomez A., and Puente J. A review of machine learning in dynamic scheduling of flexible manufacturing systems. Artif Intell Eng Des Anal Manuf 15 3 (2001) 251-263
9. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. Parallel distributed processing (1986), MIT press, Cambridge, MA
10. Arzi Y., and Iaroslavitz L. Neural network-based adaptive production control system for flexible manufacturing cell under a random environment. IIE Trans 31 3 (1999) 217-230
11. Priore P., De La Fuente D., Puente J., and Parreño J. A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Eng Appl Artif Intell 19 3 (2006) 247-255
12. Park S.C., Raman N., and Shaw M.J. Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach. IEEE Trans Robot Automat 13 4 (1997) 486-502
13. Quinlan J.R. Induction of decision trees. Mach Learn 1 1 (1986) 81-106
14. Quinlan J.R. Simplifying decision trees. Int J Man-Mach Studies 27 3 (1987) 221-234
15. Quinlan J.R. C4.5: programs for machine learning (1993), Morgan Kaufmann, San Manteo, CA
16. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning (1989), Addison-Wesley, Reading, MA
17. Chiu C., and Yih Y. A learning-based methodology for dynamic scheduling in distributed manufacturing systems. Int J Prod Res 33 11 (1995) 3217-3232
18. Kim C.O., Min H.S., and Yih Y. Integration of inductive learning and neural networks for multi-objective FMS scheduling. Int J Prod Res 36 9 (1998) 2497-2509
19. Chen C.C., Yih Y., and Wu Y.C. Auto-bias selection for learning-based scheduling systems. Int J Prod Res 37 9 (1999) 1987-2002
20. Baker K.R. Sequencing rules and due-date assignments in a job shop. Manage Sci 30 9 (1984) 1093-1104
21. Montazeri M., and Van Wassenhove L.N. Analysis of scheduling rules for an FMS. Int J Prod Res 28 4 (1990) 785-802
22. Chan F.T.S., and Chan T.S. Analysis of dynamic control strategies of an FMS under different scenarios. Robot Comput-Integrated Manuf 20 5 (2004) 423-437
23. Wu S.D., and Wysk R.A. An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing. Int J Prod Res 27 9 (1989) 1603-1623
24. Sun Z., Bebis G., and Miller R. Object detection using feature subset selection. Pattern Recognition 37 11 (2004) 2165-2176
25. Chan F.T.S., Chung S.H., and Chan P.L.Y. Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems. Int J Prod Res 44 3 (2006) 523-543
26. Chan F.T.S., Chung S.H., Chan P.L.Y., Finke G., and Tiwari M.K. Solving distributed FMS scheduling problems subject to maintenance: genetic algorithms approach. Robot Comput-Integrated Manuf 22 5-6 (2006) 493-504
27. Shiue Y.R. Data mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach. Int J Prod Res (2008) 10.1080/00207540701846236
28. Kohonen T. Self-Organizing Map. 3rd ed (2001), Springer, New York, NY
29. Vesanto J., and Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Networks 11 3 (2000) 586-600
30. Davies F.D., and Bouldin D.W. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1 2 (1979) 224-227
31. eM-Plant. Objects manual version and reference manual 7.0 (2003), Tecnomatix Technologies, Stuttgart
32. See5. See5: An informal tutorial, 〈http://www.rulequest.com/see5-win.html〉.
33. MATLAB. Matlab neural network toolbox version 5 user's guide (2007), The MathWorks Inc., Natick, MA
34. Baker K.R. Sequencing rules and due-date assignments in a job shop. Manage Sci 30 9 (1984) 1093-1104
35. Ishii N., and Talavage J.J. A mixed dispatching rule approach in FMS scheduling. Int J Flexible Manuf Syst 6 1 (1994) 69-87
36. Min H.S., and Yih Y. Development of a real-time multi-objective scheduler for a semiconductor fabrication system. Int J Prod Res 41 10 (2003) 2345-2364
37. Stutton R.S., and Barto G.B. Reinforcement learning: an introduction (1998), MIT Press, Cambridge, MA