Dynamic scheduling of manufacturing systems using machine learning: An updated review

Abstract A common way of dynamically scheduling jobs in a manufacturing system is by implementing dispatching rules. The issues with this method are that the performance of these rules depends on the state the system is in at each moment and also that no “ideal” single rule exists for all the possible states that the system may be in. Therefore, it would be interesting to use the most appropriate dispatching rule for each instance. To achieve this goal, a scheduling approach that uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a literature review of the main machine learning based scheduling approaches from the last decade is presented.

[1]  Alberto Gómez,et al.  LEARNING-BASED SCHEDULING OF FLEXIBLE MANUFACTURING SYSTEMS USING SUPPORT VECTOR MACHINES , 2010, Appl. Artif. Intell..

[2]  Harun Reşit Yazgan,et al.  Selection of dispatching rules in FMS: ANP model based on BOCR with choquet integral , 2010 .

[3]  Antonio Rizzi,et al.  A fuzzy logic based methodology to rank shop floor dispatching rules , 2002 .

[4]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[5]  Chao-Ton Su,et al.  Intelligent scheduling controller for shop floor control systems: A hybrid genetic algorithm/decision tree learning approach , 2003 .

[6]  Kenneth R. Baker,et al.  Sequencing Rules and Due-Date Assignments in a Job Shop , 1984 .

[7]  Der-Chiang Li,et al.  Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge , 2007, Comput. Oper. Res..

[8]  Yeong-Dae Kim,et al.  A real-time scheduling mechanism for a flexible manufacturing system: Using simulation and dispatching rules , 1998 .

[9]  L. N. Van Wassenhove,et al.  Analysis of Scheduling Rules for an FMS , 1990 .

[10]  Ryu Sasaki,et al.  A New Formulation , 1998 .

[11]  Yuehwern Yih,et al.  Indentifying attributes for knowledge-based development in dynamic scheduling environments , 1996 .

[12]  Ari P. J. Vepsalainen.  Priority rules for job shops with weighted tardiness costs , 1987 .

[13]  Ruey-Shiang Guh,et al.  Study of SOM-based intelligent multi-controller for real-time scheduling , 2011, Appl. Soft Comput..

[14]  Key K. Lee,et al.  Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment , 2008, Appl. Soft Comput..

[15]  Ludmil Mikhailov,et al.  Deriving priorities from fuzzy pairwise comparison judgements , 2003, Fuzzy Sets Syst..

[16]  Yeou-Ren Shiue.  Development of two-level decision tree-based real-time scheduling system under product mix variety environment , 2009 .

[17]  Taho Yang,et al.  A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing , 2010 .

[18]  Shinichi Nakasuka,et al.  Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool , 1992 .

[19]  John W. Fowler,et al.  Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling , 2003 .

[20]  Kathryn E. Stecke,et al.  Formulation and Solution of Nonlinear Integer Production Planning Problems for Flexible Manufacturing Systems , 1983 .

[21]  Dong-Ho Lee,et al.  Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line , 2011, Expert Syst. Appl..

[22]  Yeou-Ren Shiue,et al.  Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach , 2009 .

[23]  Wenxin Liu,et al.  A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment , 2005, J. Intell. Manuf..

[24]  David R. Denzler,et al.  Experimental investigation of flexible manufacturing system scheduling decision rules , 1987 .

[25]  Chinyao Low,et al.  Modelling and heuristics of FMS scheduling with multiple objectives , 2006, Comput. Oper. Res..

[26]  Ruey-Shiang Guh,et al.  The optimization of attribute selection in decision tree-based production control systems , 2006 .

[27]  Han-Pang Huang,et al.  A New Approach to On-Line Rescheduling for a Semiconductor Foundry Fab , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[28]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[29]  David de la Fuente,et al.  Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning , 2003 .

[30]  Yuehwern Yih,et al.  A study on decision rules of a scheduling model in an FMS , 1993 .

[31]  Jean-Charles Billaut,et al.  Production , Manufacturing and Logistics A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation , 2004 .

[32]  Kripa Shanker,et al.  A loading and dispatching problem in a random flexible manufacturing system , 1985 .

[33]  Stanley B. Gershwin,et al.  Flow optimization in flexible manufacturing systems , 1985 .

[34]  Claudio Moraga,et al.  A diffusion-neural-network for learning from small samples , 2004, Int. J. Approx. Reason..

[35]  Kathryn E. Stecke,et al.  Loading and control policies for a flexible manufacturing system , 1981 .

[36]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[37]  Thomas Impelluso,et al.  Simulation-Based Learning , 2006, Proceedings. Frontiers in Education. 36th Annual Conference.

[38]  Huan Liu,et al.  A Probabilistic Approach to Feature Selection - A Filter Solution , 1996, ICML.

[39]  Ihsan Sabuncuoglu,et al.  Analysis of reactive scheduling problems in a job shop environment , 2000, Eur. J. Oper. Res..

[40]  Ruey-Shiang Guh,et al.  The study of real time scheduling by an intelligent multi-controller approach , 2011 .

[41]  M. Kuhl,et al.  A Simulation Based Learning Meachanism for Scheduling Systems , 2005, Proceedings of the Winter Simulation Conference, 2005..

[42]  David de la Fuente,et al.  A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems , 2006, Eng. Appl. Artif. Intell..

[43]  Huan Neng Chiu,et al.  Vendor selection by integrated fuzzy MCDM techniques with independent and interdependent relationships , 2008, Inf. Sci..

[44]  Sally A. Goldman,et al.  Computational Learning Theory , 2010, Lecture Notes in Computer Science.

[45]  Ruey-Shiang Guh,et al.  Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment , 2006 .

[46]  Han-Pang Huang,et al.  Dynamic scheduling of flexible manufacturing system using support vector machines , 2005, IEEE International Conference on Automation Science and Engineering, 2005..

[47]  Ruey-Shiang Guh,et al.  GA-based learning bias selection mechanism for real-time scheduling systems , 2009, Expert Syst. Appl..

[48]  Ahmed El-Bouri,et al.  A neural network for dispatching rule selection in a job shop , 2006 .

[49]  Don T. Phillips,et al.  A state-of-the-art survey of dispatching rules for manufacturing job shop operations , 1982 .

[50]  Min-Hong Han,et al.  Real-time tool control and job dispatching in flexible manufacturing systems , 1989 .

[51]  C. Lévi-Strauss,et al.  Experimental investigation , 2013 .

[52]  H.-S. Yan,et al.  An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell , 2009 .

[53]  Upendra Dave,et al.  Heuristic Scheduling Systems , 1993 .

[54]  Sigurdur Olafsson,et al.  Learning effective new single machine dispatching rules from optimal scheduling data , 2010 .

[55]  D. Atkin.  OR scheduling algorithms. , 2000, Anesthesiology.

[56]  Minheekim,et al.  Simulation-based real-time scheduling in a flexible manufacturing system , 1994 .

[57]  Fengming M. Chang,et al.  Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling , 2006 .

[58]  Ihsan Sabuncuoglu,et al.  Experimental investigation of iterative simulation-based scheduling in a dynamic and stochastic job shop , 2001 .

[59]  Michael J. Shaw,et al.  Intelligent Scheduling with Machine Learning Capabilities: The Induction of Scheduling Knowledge§ , 1992 .

[60]  Der-Chiang Li,et al.  Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge , 2006, Comput. Oper. Res..

[61]  Robert T. Barrett,et al.  A SLAM II simulation study of a simplified flow shop , 1986, Simul..

[62]  Lars Mönch,et al.  Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines , 2006, Eng. Appl. Artif. Intell..

[63]  Xiaonan Li,et al.  Discovering Dispatching Rules Using Data Mining , 2005, J. Sched..

[64]  Alberto Gómez,et al.  A review of machine learning in dynamic scheduling of flexible manufacturing systems , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[65]  Nasser Mebarki,et al.  Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem , 2012, Eng. Appl. Artif. Intell..

[66]  Yi-Chi Wang,et al.  Application of reinforcement learning for agent-based production scheduling , 2005, Eng. Appl. Artif. Intell..

[67]  Lawrence W. Lan,et al.  Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral , 2009, Comput. Ind. Eng..

[68]  Peter Cowling,et al.  Production, Manufacturing and Logistics Using real time information for effective dynamic scheduling , 2002 .

[69]  Jen-Shiang Chen,et al.  Mixed binary integer programming formulations for the reentrant job shop scheduling problem , 2005, Comput. Oper. Res..

[70]  S. S. Panwalkar,et al.  A Survey of Scheduling Rules , 1977, Oper. Res..

[71]  Yuehwern Yih,et al.  Selection of dispatching rules on multiple dispatching decision points in real-time scheduling of a semiconductor wafer fabrication system , 2003 .

[72]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[73]  Yuehwern Yih,et al.  A competitive neural network approach to multi-objective FMS scheduling , 1998 .

[74]  David de la Fuente,et al.  Learning-based scheduling of flexible manufacturing systems using case-based reasoning , 2001, Appl. Artif. Intell..

[75]  Joseph J. Talavage,et al.  A transient-based real-time scheduling algorithm in FMS , 1991 .

[76]  K. D. Atkins,et al.  Real time selection. , 2003 .

[77]  Henri Pierreval,et al.  Training a neural network to select dispatching rules in real time , 2010, Comput. Ind. Eng..

[78]  Jeffrey W. Herrmann,et al.  Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods , 2003, J. Sched..

[79]  Hyunbo Cho,et al.  A robust adaptive scheduler for an intelligent workstation controller , 1993 .

[80]  Der-Chiang Li,et al.  Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy , 2005 .

[81]  Long-Sheng Chen,et al.  Using Functional Virtual Population as assistance to learn scheduling knowledge in dynamic manufacturing environments , 2003 .

[82]  Wenjian Liu,et al.  A Hybrid Inductive Learning-based Scheduling Knowledge Acquisition Algorithm , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[83]  Hao Wen Lin,et al.  Rule driven multi objective dynamic scheduling by data envelopment analysis and reinforcement learning , 2010, 2010 IEEE International Conference on Automation and Logistics.

[84]  D. Chang.  Applications of the extent analysis method on fuzzy AHP , 1996 .

[85]  David W. Aha,et al.  Towards a Better Understanding of Memory-based Reasoning Systems , 1994, ICML.

[86]  Ihsan Sabuncuoglu,et al.  A Simulation Based Learning Meachanism for Scheduling Systems , 2005, Proceedings of the Winter Simulation Conference, 2005..

[87]  Richard A. Wysk,et al.  An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing , 1989 .

[88]  Lawrence M. Wein,et al.  Scheduling semiconductor wafer fabrication , 1988 .

[89]  Muzaffer Kapanoglu,et al.  Learning IF-THEN priority rules for dynamic job shops using genetic algorithms , 2011 .

[90]  Harun Resit Yazgan,et al.  Selection of dispatching rules with fuzzy ANP approach , 2011 .

[91]  Reha Uzsoy,et al.  Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach , 2006, J. Sched..

[92]  John M. Wilson.  An alternative formulation of the operation-allocation problem in flexible manufacturing systems , 1989 .

[93]  Christopher D. Geiger,et al.  Learning effective dispatching rules for batch processor scheduling , 2008 .

[94]  F. Chana,et al.  Analysis of dynamic dispatching rules for a flexible manufacturing system , 2015 .

[95]  Yi-Chi Wang,et al.  Learning policies for single machine job dispatching , 2004 .

[96]  Henri Pierreval,et al.  Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining , 2010 .

[97]  R. S. Lashkari,et al.  A new formulation of operation allocation problem in flexible manufacturing systems: mathematical modelling and computational experience , 1987 .

[98]  H. Didehkhani,et al.  Developing a fuzzy ANP model for selecting the suitable dispatching rule for scheduling a FMS , 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.