Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection 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
• Breiman , L . 1996 . Bagging predictors . Machine Learning , 24 ( 2 ) : 123 – 140 .
   Web of Science ®Google Scholar
• Chang , CC and Lin , CJ . 2010. LIBSVM 3.0: A library for support vector machines [online]. Available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ [Accessed 1 April 2011]
   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
• Dietterich , TG . 2000 . An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, Boosting and randomisation . Machine Learning , 40 ( 2 ) : 139 – 157 .
   Web of Science ®Google Scholar
• Freund , Y and Schapire , R . Experiments with a new Boosting algorithm . Proceedings of the 13th international conference on machine learning . 3–6 July 1996 , Bari . Italy. San Francisco: Morgan Kaufmann Publishers, 148–156
   Google Scholar
• Goldberg , DE . 1989 . Genetic algorithms in search, optimisation and machine learning , Reading, MA : Addison‐Wesley .
   Google Scholar
• Hsu , CW . Chang, C.C. and Lin, C.J., 2003. A practical guide to support vector classification [online]. Available from: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf [Accessed 1 April 2011]
   Google Scholar
• Kim , Y . 2009 . Boosting and measuring the performance of ensembles for successful database marketing . Expert Systems with Applications , 36 ( 2 ) : 2161 – 2176 .
   Web of Science ®Google Scholar
• Kittler , J . 1998 . On combining classifiers . IEEE Transactions on Pattern Analysis and Machine Intelligence , 20 ( 3 ) : 226 – 239 .
   Web of Science ®Google Scholar
• Kohavi , R and John , G . 1997 . Wrappers for feature subset . Artificial Intelligence , 97 ( 1–2 ) : 273 – 324 .
   Web of Science ®Google Scholar
• Kuncheva , LI and Whitaker , CJ . 2003 . Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy . Machine Learning , 51 ( 2 ) : 181 – 207 .
   Web of Science ®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
• Lin , H . 2006 . Content‐based image retrieval trained by Adaboost for mobile application . International Journal of Pattern Recognition and Artificial Intelligence , 20 ( 4 ) : 525 – 541 .
   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
• Mangiameli , P , West , D and Rampal , R . 2004 . Model selection for medical diagnosis decision support systems . Decision Support Systems , 36 ( 3 ) : 247 – 259 .
   Web of Science ®Google Scholar
• Min , SH , Lee , J and Han , I . 2006 . Hybrid genetic algorithms and support vector machines for bankruptcy prediction . Expert System with Applications , 31 ( 3 ) : 652 – 660 .
   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
• Opitz , D and Maclin , R . 1999 . Popular ensemble methods: An empirical study . Journal of Artificial Intelligence Research , 11 ( 1 ) : 169 – 198 .
   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
• Priore , P . 2010 . Learning‐based scheduling of flexible manufacturing systems using support vector machines . Applied Artificial Intelligence , 24 ( 3 ) : 194 – 209 .
   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
• Rokach , L . 2010 . Pattern classification using ensemble methods , Singapore : World Scientific Publishing .
   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
• Shiue , YR , Guh , RS and Tseng , TY . 2009 . GA‐based learning bias selection mechanism for real time scheduling systems . Expert Systems with Applications , 36 ( 9 ) : 11451 – 11460 .
   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
• Son , Y , Rodríguez‐Rivera , H and Wysk , RA . 1999 . A multi‐pass simulation‐based, real‐time scheduling and shop floor control system . Transactions: The Quarterly Journal of the Society for Computer Simulation International , 16 ( 4 ) : 159 – 172 .
   Google Scholar
• Son , Y , Wysk , RA and Jones , A . 2003 . Simulation based shop floor control: formal model, model generation and control interface . IIE Transactions , 35 ( 1 ) : 29 – 48 .
   Web of Science ®Google Scholar
• Stutton , RS and Barto , GB . 1998 . Reinforcement learning: An introduction , Cambridge : MIT Press .
   Google Scholar
• Su , CT and Lin , HC . 2011 . Applying electromagnetism‐like mechanism for feature selection . Information Sciences , 181 ( 5 ) : 972 – 986 .
   Web of Science ®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 . 2009 . Tecnomatix plant simulation 9.0 user guide , Plano : Tecnomatix Technologies Ltd .
   Google Scholar
• Vapnik , VN . 1999 . The nature of statistical learning Theory , 2nd , New York : Springer .
   Google Scholar
• Witten , IH , Frank , E and Hall , MA . 2011 . Data mining: practical machine learning tools and techniques , 3rd , Burlington : Morgan Kaufmann .
   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
• Yang , J and Honavar , V . 1998 . Feature subset selection using a genetic algorithm . IEEE Intelligent Systems , 13 ( 2 ) : 44 – 49 .
   Web of Science ®Google Scholar