Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Robotics and Computer-Integrated Manufacturing

Volumn 22, Issue 3, 2006, Pages 203-216

  Shiue, Yeou Ren ^a   Guh, Ruey Shiang ^b  

^a HUAFAN UNIVERSITY   (Taiwan)

^b NATIONAL FORMOSA UNIVERSITY   (Taiwan)

Author keywords

Adaptive scheduling; Artificial neural network; Feature selection; Generalization ability; Genetic algorithm; Machine learning

Indexed keywords

GENETIC ALGORITHMS; NEURAL NETWORKS; PROBLEM SOLVING; SCHEDULING; TOPOLOGY;

ADAPTIVE SCHEDULING; FEATURE SELECTION; GENERALIZATION ABILITY;

LEARNING SYSTEMS;

EID: 32544433960     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2005.03.004     Document Type: Article

References (43)

1. H. Cho, and R.A. Wysk A robust adaptive scheduler for an intelligent workstation controller Int. J. of Prod. Res. 31 4 1993 771 789
2. C. Chiu, and Y. Yih A learning-based methodology for dynamic scheduling in distributed manufacturing systems Int. J. Prod. Res. 33 11 1995 3217 3232
3. S.C. Park, N. Raman, and M.J. Shaw Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach IEEE Trans. Robotics Automat. 13 4 1997 486 502
4. P. Priore, D. De la Fuente, A. Gomez, and J. Puente A review of machine learning in dynamic scheduling of flexible manufacturing systems Artif. Intell. Eng. Design, Analy. Manuf. 15 3 2001 251 263
5. J.H. Blackstone, D.T. Philips Jr, and G.L. Hogg A state-of-the-art survey of dispatching rules for manufacturing job shop operations Int. J. Prod. Res. 20 1 1982 27 45
6. K.R. Baker Sequencing rules and due-date assignments in a job shop Manage. Sci. 30 9 1984 1093 1104
7. M. Montazeri, and L.N. Van Wassenhove Analysis of scheduling rules for an FMS Int. J. Prod. Res. 28 4 1990 785 802
8. I. Sabuncuoglu A study of scheduling rules of flexible manufacturing systems: a simulation approach Int. J. Prod. Res. 36 5 1998 527 546
9. S.D. Wu, and R.A. Wysk Multi-pass expert control system - a control/scheduling structure for flexible manufacturing cells J. Manuf. Sys. 7 2 1988 1603 1623
10. S.D. Wu, and R.A. Wysk An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing Int. J. Prod. Res. 27 9 1989 1603 1623
11. H. Cho, and R.A. Wysk Intelligent workstation controller for computer-integrated manufacturing: problems and models J. Manuf. Sys. 14 4 1995 252 263
12. M.J. Shaw, S. Park, and N. Raman Intelligent scheduling with machine learning capabilities: the induction of scheduling knowledge IIE Trans 24 2 1992 156 168
13. C.O. Kim, H.S. Min, and Y. Yih Integration of inductive learning and neural networks for multi-objective FMS scheduling Int. J. Prod. Res. 36 9 1998 2497 2509
14. Y. Arzi, and L. Iaroslavitz Operating an FMC by a decision-tree-based adaptive production control system Int. J. Prod. Res. 38 3 2000 675 697
15. Y.R. Shiue, and C.T. Su 2003, An enhanced knowledge representation for decision-tree based learning adaptive scheduling Int. J. Comput. Integr. Manuf. 16 1 2003 48 60
16. C.T. Su, and Y.R. Shiue 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
17. J.R. Quinlan Induction of decision trees Mach. Learning 1 1 1986 81 106
18. J.R. Quinlan Simplifying decision trees Int. J. Man-Mach. Stud. 27 3 1987 221 234
19. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann San Manteo, CA
20. D.E. Rumelhart, G.E. Hinton, and R.J. Williams Learning internal representations by error propagation 1986 MIT press Parallel Distributed Processing. Cambridge, MA
21. Y.L. Sun, and Y. YIH An intelligent controller for manufacturing cells Int. J. Prod. Res. 34 8 1996 2353 2373
22. Y. Arzi, and L. Iaroslavitz Neural network-based adaptive production control system for flexible manufacturing cell under a random environment IIE Trans 31 3 1999 217 230
23. C.C. Chen, and Y. Yih Identifying attributes for knowledge-based development in dynamic scheduling environments Int. J. Prod. Res. 34 6 1996 1739 1755
24. C.C. Chen, Y. Yih, and Y.C. Wu Auto-bias selection for learning-based scheduling systems Int. J. Prod. Res. 37 9 1999 1987 2002
25. T.M. Cover The best two independent measurements are not the two best IEEE Trans. Syst. Man. Cybernet. 4 1974 116 117
26. W. Siedlecki, and J. Sklansky On Automatic feature selection Int. J. Pattern Recognition Artif. Intell. 2 2 1988 197 220
27. R.S. Sexton, R.E. Dorsey, and J.D. Johnson Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing Eur. J. Oper. Res. 114 3 1999 589 601
28. A. Blanco, M. Delgado, and M.C. Pegalajar A real-coded genetic algorithm for training recurrent neural networks Neural Networks 14 1 2001 93 105
29. S.A. Billings, and G.L. Zheng Radial Basis function network configuration using genetic algorithms Neural Networks 8 6 1995 877 890
30. J. Han, C. Moraga, and S. Sinne Optimization of feedforward neural networks Eng. Appl. Artif. Intell. 9 2 1996 109 119
31. J. Arifovic, and R. Gencay Using genetic algorithms to select architecture of a feedforward artificial neural network Physica A 289 3-4 2001 574 594
32. W.E. Wilhelm, and H.M. Shin Effectiveness of alternative operations in a flexible manufacturing system Int. J. Prod. Res. 23 1 1985 65 79
33. G.Y. Lin, and J. Solberg Effective of flexible routing control The Int. J. Flexible Manuf. Syst. 3 1991 189 211
34. F.T. Chan Evaluations of operational control rules in scheduling a flexible manufacturing system Robotics Comput.-Integrated Manuf. 15 1999 121 132
35. D.E. Goldberg Genetic algorithms in search, optimization, and machine learning 1989 Addison-Wesley Reading MA
36. NeuralWare Neuralworks professional plus reference guide 2002 NeuralWare Inc. Pittsburgh, PA
37. T.M. Mitchell Machine learning 1997 McGraw-Hill New York, NY
38. DeJong K. An analysis and behavior of a class of genetic adaptive systems. Ph.D. Thesis. Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1975.
39. D.W. Patterson Artificial neural networks: theory and applications 1996 Prentice-Hall Singapore
40. SIMPLE ++. Reference Manual Version 7.0; Stuttgart: AESOP; 2000.
41. Evolver Guide to evolver 1998 Palisade Corporation Newfield, NY
42. L.R. Medsker Hybrid intelligent systems 1995 Kluwer Academic Publishers Boston, MA
43. See5. See5: An informal tutorial, http://www.rulequest.com/see5-win.html.