The study of real time scheduling by an intelligent multi-controller approach

References

• Arzi , Y and Iaroslavitz , L . 1999 . Neural network-based adaptive production control system for flexible manufacturing cell under a random environment . IIE Transactions , 31 ( 3 ) : 217 – 230 .
   Web of Science ®Google Scholar
• Arzi , Y and Iaroslavitz , L . 2000 . Operating an FMC by a decision-tree-based adaptive production control system . International Journal of Production Research , 38 ( 3 ) : 675 – 697 .
   Web of Science ®Google Scholar
• Baker , KR . 1984 . Sequencing rules and due-date assignments in a job shop . Management Science , 30 ( 9 ) : 1093 – 1104 .
   Web of Science ®Google Scholar
• Chang , CC and Lin , CJ . 2006 . LIBSVM 2.82: A library for support vector machines [online] . Available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ [Accessed 10 April 2006]
   Google Scholar
• Chen , CC , Yih , Y and Wu , YC . 1999 . Auto-bias selection for learning-based scheduling systems . International Journal of Production Research , 37 ( 9 ) : 1987 – 2002 .
   Web of Science ®Google Scholar
• Cho , H and Wysk , RA . 1993 . A robust adaptive scheduler for an intelligent workstation controller . International Journal of Production Research , 31 ( 4 ) : 771 – 789 .
   Web of Science ®Google Scholar
• Cho , H and Wysk , RA . 1995 . Intelligent workstation controller for computer-integrated manufacturing: problems and models . Journal of Manufacturing Systems , 14 ( 4 ) : 252 – 263 .
   Web of Science ®Google Scholar
• Davies , FD and Bouldin , DW . 1979 . A cluster separation measure . IEEE Transactions on Pattern Analysis and Machine Intelligence , 1 ( 2 ) : 224 – 227 .
   PubMed Web of Science ®Google Scholar
• Goldberg , DE . 1989 . Genetic Algorithms in search, optimisation and machine learning , Reading, MA : Addison-Wesley .
   Google Scholar
• Han , J and Kamber , M . 2006 . Data mining concepts and techniques, , 2nd , San Francisco : Morgan Kaufmann .
   Google Scholar
• Ishii , N and Talavage , JJ . 1994 . A mixed dispatching rule approach in FMS scheduling . International Journal of Flexible Manufacturing Systems , 6 ( 1 ) : 69 – 87 .
   Google Scholar
• Kim , CO , Min , HS and Yih , Y . 1998 . Integration of inductive learning and neural networks for multi-objective FMS scheduling . International Journal of Production Research , 36 ( 9 ) : 2497 – 2509 .
   Web of Science ®Google Scholar
• Kohonen , T . 2001 . Self-organising map, , 3rd , New York : Springer-Verlag .
   Google Scholar
• Kwak , C and Yih , Y . 2004 . Data mining approach to production control in the computer integrated testing cell . IEEE Transactions on Robotics and Automation , 20 ( 1 ) : 107 – 116 .
   Google Scholar
• Lee , J . 2003 . E-manufacturing – fundamental, tools, and transformation . Robotics and Computer-Integrated Manufacturing , 19 ( 6 ) : 501 – 507 .
   Web of Science ®Google Scholar
• Liu , H and Motoda , H . 1998 . Feature selection for knowledge discovery and data mining , Boston : Kluwer Academic Publishers .
   Google Scholar
• Liu , H and Setiono , R . A probabilistic approach to feature selection – a filter solution . Proceedings of the 13th international conference on machine learning . 3–6 July 1996 , Bari, Italy. pp. 319 – 327 . San Francisco : Morgan Kaufmann Publishers .
   Google Scholar
• Liu , J and MacCarthy , BL . 1996 . The classification of FMS scheduling problems . International Journal of Production Research , 34 ( 3 ) : 647 – 656 .
   Web of Science ®Google Scholar
• MathWorks Inc. 2006 . Matlab version 7.1 user's guide , Natick, MA : MathWorks .
   Google Scholar
• MathWorks Inc. 2007 . Matlab neural network toolbox version 5 user's guide , Natick, MA : MathWorks .
   Google Scholar
• Min , HS and Yih , Y . 2003 . Development of a real-time multi-objective scheduler for a semiconductor fabrication system . International Journal of Production Research , 41 ( 16 ) : 2345 – 2364 .
   Web of Science ®Google Scholar
• Montazeri , M and Van Wassenhove , LN . 1990 . Analysis of scheduling rules for an FMS . International Journal of Production Research , 28 ( 4 ) : 785 – 802 .
   Web of Science ®Google Scholar
• Park , SC , Raman , N and Shaw , MJ . 1997 . Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach . IEEE Transactions on Robotics and Automation , 13 ( 4 ) : 486 – 502 .
   Google Scholar
• Priore , P . 2001 . A review of machine learning in dynamic scheduling of flexible manufacturing systems . Artificial Intelligence for Engineering Design, Analysis & Manufacturing , 15 ( 3 ) : 251 – 263 .
   Web of Science ®Google Scholar
• Priore , P . 2006 . A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems . Engineering Applications of Artificial Intelligence , 19 ( 3 ) : 247 – 255 .
   Web of Science ®Google Scholar
• Qiu , R , Wysk , R and Xu , Q . 2003 . Extended structured adaptive supervisory control model of shop floor controls for an e-Manufacturing system . International Journal of Production Research , 41 ( 8 ) : 1605 – 1620 .
   Web of Science ®Google Scholar
• Quinlan , JR . 1986 . Induction of decision trees . Machine Learning , 1 ( 1 ) : 81 – 106 .
   Google Scholar
• Quinlan , JR . 1987 . Simplifying decision trees . International Journal of Man–Machine Studies , 27 ( 3 ) : 221 – 234 .
   Google Scholar
• Quinlan , JR . 1993 . C4.5: Programs for machine learning , San Manteo : Morgan Kaufmann .
   Google Scholar
• Rumelhart , DE , Hinton , GE and Williams , RJ . 1986 . Learning internal representations by error propagation. Parallel distributed processing , Cambridge : MA Press .
   Google Scholar
• Shiue , YR . 2009a . Data mining-based dynamic dispatching rule selection mechanism for shop floor control systems using support vector machine approach . International Journal of Production Research , 47 ( 13 ) : 3669 – 3690 .
   Web of Science ®Google Scholar
• Shiue , YR . 2009b . Development of two-level decision tree-based real-time scheduling system under product mix variety environment . Robotics and Computer-Integrated Manufacturing , 25 ( 4–5 ) : 709 – 720 .
   Web of Science ®Google Scholar
• Shiue , YR and Guh , RS . 2006 . Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment . Robotics and Computer-Integrated Manufacturing , 22 ( 3 ) : 203 – 216 .
   Web of Science ®Google Scholar
• Shnits , B and Sinreich , D . 2006 . Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy . International Journal of Flexible Manufacturing Systems , 18 ( 1 ) : 1 – 27 .
   Google Scholar
• Stutton , RS and Barto , GB . 1998 . Reinforcement learning: An introduction , Cambridge : MIT Press .
   Google Scholar
• Su , CT and Shiue , YR . 2003 . Intelligent scheduling controller for shop floor control systems: a hybrid genetic algorithm/decision tree learning approach . International Journal of Production Research , 41 ( 12 ) : 2619 – 2641 .
   Web of Science ®Google Scholar
• Tecnomatix Plant Simulation . 2006 . Tecnomatix plant simulation 7.6 user guide , Plano : Tecnomatix Technologies Ltd .
   Google Scholar
• Vapnik , VN . 1999 . The nature of statistical learning theory, , 2nd , New York : Springer .
   Google Scholar
• Vesanto , J and Alhoniemi , E . 2000 . Clustering of the self-organising map . IEEE Transactions on Neural Networks , 11 ( 3 ) : 586 – 600 .
   PubMed Web of Science ®Google Scholar
• Wu , SD and Wysk , RA . 1989 . An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing . International Journal of Production Research , 27 ( 9 ) : 1603 – 1623 .
   Web of Science ®Google Scholar